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Sample records for kalman filtering algorithm

  1. Power system static state estimation using Kalman filter algorithm

    Directory of Open Access Journals (Sweden)

    Saikia Anupam

    2016-01-01

    Full Text Available State estimation of power system is an important tool for operation, analysis and forecasting of electric power system. In this paper, a Kalman filter algorithm is presented for static estimation of power system state variables. IEEE 14 bus system is employed to check the accuracy of this method. Newton Raphson load flow study is first carried out on our test system and a set of data from the output of load flow program is taken as measurement input. Measurement inputs are simulated by adding Gaussian noise of zero mean. The results of Kalman estimation are compared with traditional Weight Least Square (WLS method and it is observed that Kalman filter algorithm is numerically more efficient than traditional WLS method. Estimation accuracy is also tested for presence of parametric error in the system. In addition, numerical stability of Kalman filter algorithm is tested by considering inclusion of zero mean errors in the initial estimates.

  2. A Novel Robust Interval Kalman Filter Algorithm for GPS/INS Integrated Navigation

    Directory of Open Access Journals (Sweden)

    Chen Jiang

    2016-01-01

    Full Text Available Kalman filter is widely applied in data fusion of dynamic systems under the assumption that the system and measurement noises are Gaussian distributed. In literature, the interval Kalman filter was proposed aiming at controlling the influences of the system model uncertainties. The robust Kalman filter has also been proposed to control the effects of outliers. In this paper, a new interval Kalman filter algorithm is proposed by integrating the robust estimation and the interval Kalman filter in which the system noise and the observation noise terms are considered simultaneously. The noise data reduction and the robust estimation methods are both introduced into the proposed interval Kalman filter algorithm. The new algorithm is equal to the standard Kalman filter in terms of computation, but superior for managing with outliers. The advantage of the proposed algorithm is demonstrated experimentally using the integrated navigation of Global Positioning System (GPS and the Inertial Navigation System (INS.

  3. Kalman filter based algorithms for PANDA rate at FAIR

    Energy Technology Data Exchange (ETDEWEB)

    Prencipe, Elisabetta; Ritman, James [IKP, Forschungszentrum Juelich (Germany); Rauch, Johannes [E18, Technische Universitaet Muenchen (Germany); Collaboration: PANDA-Collaboration

    2015-07-01

    PANDA at the future FAIR facility in Darmstadt is an experiment with a cooled antiproton beam in a range between 1.5 and 15 GeV/c, allowing a wide physics program in nuclear and particle physics. High average reaction rates up to 2.10{sup 7} interactions/s are expected. PANDA is the only experiment worldwide, which combines a solenoid field and a dipole field in an experiment with a fixed target topology. The tracking system must be able to reconstruct high momenta in the laboratory frame. The tracking system of PANDA involves the presence of a high performance silicon vertex detector, a GEM detector, a Straw-Tubes central tracker, a forward tracking system, and a luminosity monitor. The first three of those, are inserted in a solenoid homogeneous magnetic field (B=2 T), the latter two are inside a dipole magnetic field (B=2 Tm), The offline tracking algorithm is developed within the PandaRoot framework, which is a part of the FAIRRoot project. The algorithm is based on a tool containing the Kalman Filter equations and a deterministic annealing filter (GENFIT). The Kalman-Filter-based routines can perform extrapolations of track parameters and covariance matrices. In GENFIT2, the Runge-Kutta track representation is available. First results of an implementation of GENFIT2 in PandaRoot are presented. Resolutions and efficiencies for different beam momenta and different track hypotheses are shown.

  4. Generic Kalman Filter Software

    Science.gov (United States)

    Lisano, Michael E., II; Crues, Edwin Z.

    2005-01-01

    The Generic Kalman Filter (GKF) software provides a standard basis for the development of application-specific Kalman-filter programs. Historically, Kalman filters have been implemented by customized programs that must be written, coded, and debugged anew for each unique application, then tested and tuned with simulated or actual measurement data. Total development times for typical Kalman-filter application programs have ranged from months to weeks. The GKF software can simplify the development process and reduce the development time by eliminating the need to re-create the fundamental implementation of the Kalman filter for each new application. The GKF software is written in the ANSI C programming language. It contains a generic Kalman-filter-development directory that, in turn, contains a code for a generic Kalman filter function; more specifically, it contains a generically designed and generically coded implementation of linear, linearized, and extended Kalman filtering algorithms, including algorithms for state- and covariance-update and -propagation functions. The mathematical theory that underlies the algorithms is well known and has been reported extensively in the open technical literature. Also contained in the directory are a header file that defines generic Kalman-filter data structures and prototype functions and template versions of application-specific subfunction and calling navigation/estimation routine code and headers. Once the user has provided a calling routine and the required application-specific subfunctions, the application-specific Kalman-filter software can be compiled and executed immediately. During execution, the generic Kalman-filter function is called from a higher-level navigation or estimation routine that preprocesses measurement data and post-processes output data. The generic Kalman-filter function uses the aforementioned data structures and five implementation- specific subfunctions, which have been developed by the user on

  5. Research and Application on Fractional-Order Darwinian PSO Based Adaptive Extended Kalman Filtering Algorithm

    Directory of Open Access Journals (Sweden)

    Qiguang Zhu

    2014-05-01

    Full Text Available To resolve the difficulty in establishing accurate priori noise model for the extended Kalman filtering algorithm, propose the fractional-order Darwinian particle swarm optimization (PSO algorithm has been proposed and introduced into the fuzzy adaptive extended Kalman filtering algorithm. The natural selection method has been adopted to improve the standard particle swarm optimization algorithm, which enhanced the diversity of particles and avoided the premature. In addition, the fractional calculus has been used to improve the evolution speed of particles. The PSO algorithm after improved has been applied to train fuzzy adaptive extended Kalman filter and achieve the simultaneous localization and mapping. The simulation results have shown that compared with the geese particle swarm optimization training of fuzzy adaptive extended Kalman filter localization and mapping algorithm, has been greatly improved in terms of localization and mapping.

  6. An Improved Interacting Multiple Model Filtering Algorithm Based on the Cubature Kalman Filter for Maneuvering Target Tracking

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    Wei Zhu

    2016-06-01

    Full Text Available In order to improve the tracking accuracy, model estimation accuracy and quick response of multiple model maneuvering target tracking, the interacting multiple models five degree cubature Kalman filter (IMM5CKF is proposed in this paper. In the proposed algorithm, the interacting multiple models (IMM algorithm processes all the models through a Markov Chain to simultaneously enhance the model tracking accuracy of target tracking. Then a five degree cubature Kalman filter (5CKF evaluates the surface integral by a higher but deterministic odd ordered spherical cubature rule to improve the tracking accuracy and the model switch sensitivity of the IMM algorithm. Finally, the simulation results demonstrate that the proposed algorithm exhibits quick and smooth switching when disposing different maneuver models, and it also performs better than the interacting multiple models cubature Kalman filter (IMMCKF, interacting multiple models unscented Kalman filter (IMMUKF, 5CKF and the optimal mode transition matrix IMM (OMTM-IMM.

  7. An Improved Interacting Multiple Model Filtering Algorithm Based on the Cubature Kalman Filter for Maneuvering Target Tracking.

    Science.gov (United States)

    Zhu, Wei; Wang, Wei; Yuan, Gannan

    2016-06-01

    In order to improve the tracking accuracy, model estimation accuracy and quick response of multiple model maneuvering target tracking, the interacting multiple models five degree cubature Kalman filter (IMM5CKF) is proposed in this paper. In the proposed algorithm, the interacting multiple models (IMM) algorithm processes all the models through a Markov Chain to simultaneously enhance the model tracking accuracy of target tracking. Then a five degree cubature Kalman filter (5CKF) evaluates the surface integral by a higher but deterministic odd ordered spherical cubature rule to improve the tracking accuracy and the model switch sensitivity of the IMM algorithm. Finally, the simulation results demonstrate that the proposed algorithm exhibits quick and smooth switching when disposing different maneuver models, and it also performs better than the interacting multiple models cubature Kalman filter (IMMCKF), interacting multiple models unscented Kalman filter (IMMUKF), 5CKF and the optimal mode transition matrix IMM (OMTM-IMM).

  8. Research on Kalman Filtering Algorithm for Deformation Information Series of Similar Single-Difference Model

    Institute of Scientific and Technical Information of China (English)

    L(U) Wei-cai; XU Shao-quan

    2004-01-01

    Using similar single-difference methodology(SSDM) to solve the deformation values of the monitoring points, there is unstability of the deformation information series, at sometimes.In order to overcome this shortcoming, Kalman filtering algorithm for this series is established,and its correctness and validity are verified with the test data obtained on the movable platform in plane. The results show that Kalman filtering can improve the correctness, reliability and stability of the deformation information series.

  9. Application of the Extended Kalman filter to fuzzy modeling : algorithms and practical implementation

    OpenAIRE

    2011-01-01

    Modeling phase is fundamental both in the analysis process of a dynamic system and the design of a control system. If this phase is in-line is even more critical and the only information of the system comes from input/output data. Some adaptation algorithms for fuzzy system based on extended Kalman filter are presented in this paper, which allows obtaining accurate models without renounce the computational efficiency that characterizes the Kalman filter, and allows ...

  10. Combination of Kalman Filtering Algorithm%一种组合式的Kalman滤波算法

    Institute of Scientific and Technical Information of China (English)

    余翔; 冯璐; 漆晶

    2013-01-01

    Because the noise impact and process signals in Kalman filtering can't be directly observed,a kind of combination of Kalman filtering algorithm is proposed.Firstly,the observation data is adaptively weighted fused.Secondly,the fusion results as a priori estimated value of the second step Kalman filtering is filtered.The adaptive algorithm combined with the Kalman algorithm improves the accuracy and precision.Finally,simulations confirme the effectiveness of the algorithm.%针对Kalman滤波算法在估计过程中存在噪声影响和过程信号无法直接观测等问题,提出一种组合式的Kalman滤波算法.首先对观测的数据进行自适应加权融合,然后将融合的结果作为第二级Kalman滤波的先验估计值,进行Kalman滤波.通过自适应算法与Kalman算法的组合算法进行数据融合,可以提高融合的准确度和精度.最后通过仿真证实算法的有效性.

  11. Adaptive system noise covariance for performance enhancement of Kalman filter-based algorithms

    Science.gov (United States)

    Lee, Vika; Chan, Keith C. C.; Leung, Henry

    1996-06-01

    Several designs of Kalman filters and the interacting multiple models algorithm were used in real tracking tasks involving high dynamic targets. The data were obtained through the joint effort of the defense departments of Canada and the US. Their performance, measured in terms of positional deviation and the number of track losses, are rather unsatisfactory even though they perform particularly well when using simulated data. To identify the reasons behind, we compared and analyzed the differences between the model assumptions behind the design of these Kalman filters and the model required for accurate tracking of these targets. In this paper, we discussed our findings. Moreover, based on the characteristics of real tracking data, we present an alternative methodology for measuring the effectiveness of various Kalman filter based trackers in stressful environmental. It can also be used to explain the well known characteristics of Kalman filter. A lower bound for the deviation, obtained from this equation, shows that deviation could be too large to manage if noise bandwidth is as high as the real data instead of a pre-assumed magnitude. Instead of having to redesign many existing Kalman filters to suit for stressful environment, we developed a design-independent module that can be added to different types of Kalman filters based trackers to enhance their performance in the tracking high dynamic targets. The module is called adaptive systems noise covariance estimation. It is not only safe (i.e. almost no negative effect) but it can sometimes even double the performance of trackers in stressful environment.

  12. Mass Conservation and Positivity Preservation with Ensemble-type Kalman Filter Algorithms

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    Janjic, Tijana; McLaughlin, Dennis B.; Cohn, Stephen E.; Verlaan, Martin

    2013-01-01

    Maintaining conservative physical laws numerically has long been recognized as being important in the development of numerical weather prediction (NWP) models. In the broader context of data assimilation, concerted efforts to maintain conservation laws numerically and to understand the significance of doing so have begun only recently. In order to enforce physically based conservation laws of total mass and positivity in the ensemble Kalman filter, we incorporate constraints to ensure that the filter ensemble members and the ensemble mean conserve mass and remain nonnegative through measurement updates. We show that the analysis steps of ensemble transform Kalman filter (ETKF) algorithm and ensemble Kalman filter algorithm (EnKF) can conserve the mass integral, but do not preserve positivity. Further, if localization is applied or if negative values are simply set to zero, then the total mass is not conserved either. In order to ensure mass conservation, a projection matrix that corrects for localization effects is constructed. In order to maintain both mass conservation and positivity preservation through the analysis step, we construct a data assimilation algorithms based on quadratic programming and ensemble Kalman filtering. Mass and positivity are both preserved by formulating the filter update as a set of quadratic programming problems that incorporate constraints. Some simple numerical experiments indicate that this approach can have a significant positive impact on the posterior ensemble distribution, giving results that are more physically plausible both for individual ensemble members and for the ensemble mean. The results show clear improvements in both analyses and forecasts, particularly in the presence of localized features. Behavior of the algorithm is also tested in presence of model error.

  13. STATISTICAL CHARACTERISTICS INVESTIGATION OF PREDICTION ERRORS FOR INTERFEROMETRIC SIGNAL IN THE ALGORITHM OF NONLINEAR KALMAN FILTERING

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    E. L. Dmitrieva

    2016-05-01

    Full Text Available Basic peculiarities of nonlinear Kalman filtering algorithm applied to processing of interferometric signals are considered. Analytical estimates determining statistical characteristics of signal values prediction errors were obtained and analysis of errors histograms taking into account variations of different parameters of interferometric signal was carried out. Modeling of the signal prediction procedure with known fixed parameters and variable parameters of signal in the algorithm of nonlinear Kalman filtering was performed. Numerical estimates of prediction errors for interferometric signal values were obtained by formation and analysis of the errors histograms under the influence of additive noise and random variations of amplitude and frequency of interferometric signal. Nonlinear Kalman filter is shown to provide processing of signals with randomly variable parameters, however, it does not take into account directly the linearization error of harmonic function representing interferometric signal that is a filtering error source. The main drawback of the linear prediction consists in non-Gaussian statistics of prediction errors including cases of random deviations of signal amplitude and/or frequency. When implementing stochastic filtering of interferometric signals, it is reasonable to use prediction procedures based on local statistics of a signal and its parameters taken into account.

  14. Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model

    OpenAIRE

    Barthélémy, S.; Ricci, S.; Pannekoucke, O.; Thual, O.; Malaterre, P.O.

    2013-01-01

    This study describes the emulation of an Ensemble Kalman Filter (EnKF) algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically wit...

  15. Kalman Filtering with Real-Time Applications

    CERN Document Server

    Chui, Charles K

    2009-01-01

    Kalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering.

  16. Conservation of Mass and Preservation of Positivity with Ensemble-Type Kalman Filter Algorithms

    Science.gov (United States)

    Janjic, Tijana; Mclaughlin, Dennis; Cohn, Stephen E.; Verlaan, Martin

    2014-01-01

    This paper considers the incorporation of constraints to enforce physically based conservation laws in the ensemble Kalman filter. In particular, constraints are used to ensure that the ensemble members and the ensemble mean conserve mass and remain nonnegative through measurement updates. In certain situations filtering algorithms such as the ensemble Kalman filter (EnKF) and ensemble transform Kalman filter (ETKF) yield updated ensembles that conserve mass but are negative, even though the actual states must be nonnegative. In such situations if negative values are set to zero, or a log transform is introduced, the total mass will not be conserved. In this study, mass and positivity are both preserved by formulating the filter update as a set of quadratic programming problems that incorporate non-negativity constraints. Simple numerical experiments indicate that this approach can have a significant positive impact on the posterior ensemble distribution, giving results that are more physically plausible both for individual ensemble members and for the ensemble mean. In two examples, an update that includes a non-negativity constraint is able to properly describe the transport of a sharp feature (e.g., a triangle or cone). A number of implementation questions still need to be addressed, particularly the need to develop a computationally efficient quadratic programming update for large ensemble.

  17. A SLAM Algorithm Based on Adaptive Cubature Kalman Filter

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    Fei Yu

    2014-01-01

    CKF-SLAM and the adaptive estimator, the new ACKF-SLAM algorithm can reduce the state estimated error significantly and improve the navigation accuracy of the SLAM system effectively. The performance of this new algorithm has been examined through numerical simulations in different scenarios. The results have shown that the position error can be effectively reduced with the new adaptive CKF-SLAM algorithm. Compared with other traditional SLAM methods, the accuracy of the nonlinear SLAM system is significantly improved. It verifies that the proposed ACKF-SLAM algorithm is valid and feasible.

  18. Multilevel ensemble Kalman filter

    KAUST Repository

    Chernov, Alexey

    2016-01-06

    This work embeds a multilevel Monte Carlo (MLMC) sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF). In terms of computational cost vs. approximation error the asymptotic performance of the multilevel ensemble Kalman filter (MLEnKF) is superior to the EnKF s.

  19. Optimization Algorithm for Kalman Filter Exploiting the Numerical Characteristics of SINS/GPS Integrated Navigation Systems

    Directory of Open Access Journals (Sweden)

    Shaoxing Hu

    2015-11-01

    Full Text Available Aiming at addressing the problem of high computational cost of the traditional Kalman filter in SINS/GPS, a practical optimization algorithm with offline-derivation and parallel processing methods based on the numerical characteristics of the system is presented in this paper. The algorithm exploits the sparseness and/or symmetry of matrices to simplify the computational procedure. Thus plenty of invalid operations can be avoided by offline derivation using a block matrix technique. For enhanced efficiency, a new parallel computational mechanism is established by subdividing and restructuring calculation processes after analyzing the extracted “useful” data. As a result, the algorithm saves about 90% of the CPU processing time and 66% of the memory usage needed in a classical Kalman filter. Meanwhile, the method as a numerical approach needs no precise-loss transformation/approximation of system modules and the accuracy suffers little in comparison with the filter before computational optimization. Furthermore, since no complicated matrix theories are needed, the algorithm can be easily transplanted into other modified filters as a secondary optimization method to achieve further efficiency.

  20. Optimization Algorithm for Kalman Filter Exploiting the Numerical Characteristics of SINS/GPS Integrated Navigation Systems.

    Science.gov (United States)

    Hu, Shaoxing; Xu, Shike; Wang, Duhu; Zhang, Aiwu

    2015-11-11

    Aiming at addressing the problem of high computational cost of the traditional Kalman filter in SINS/GPS, a practical optimization algorithm with offline-derivation and parallel processing methods based on the numerical characteristics of the system is presented in this paper. The algorithm exploits the sparseness and/or symmetry of matrices to simplify the computational procedure. Thus plenty of invalid operations can be avoided by offline derivation using a block matrix technique. For enhanced efficiency, a new parallel computational mechanism is established by subdividing and restructuring calculation processes after analyzing the extracted "useful" data. As a result, the algorithm saves about 90% of the CPU processing time and 66% of the memory usage needed in a classical Kalman filter. Meanwhile, the method as a numerical approach needs no precise-loss transformation/approximation of system modules and the accuracy suffers little in comparison with the filter before computational optimization. Furthermore, since no complicated matrix theories are needed, the algorithm can be easily transplanted into other modified filters as a secondary optimization method to achieve further efficiency.

  1. Quaternion-Based Kalman Filter for AHRS Using an Adaptive-Step Gradient Descent Algorithm

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    Li Wang

    2015-09-01

    Full Text Available This paper presents a quaternion-based Kalman filter for real-time estimation of the orientation of a quadrotor. Quaternions are used to represent rotation relationship between navigation frame and body frame. Processing of a 3-axis accelerometer using Adaptive-Step Gradient Descent (ASGD produces a computed quaternion input to the Kalman filter. The step-size in GD is set in direct proportion to the physical orientation rate. Kalman filter combines 3-axis gyroscope and computed quaternion to determine pitch and roll angles. This combination overcomes linearization error of the measurement equations and reduces the calculation cost. 3-axis magnetometer is separated from ASGD to independently calculate yaw angle for Attitude Heading Reference System (AHRS. This AHRS algorithm is able to remove the magnetic distortion impact. Experiments are carried out in the small-size flight controller and the real world flying test shows the proposed AHRS algorithm is adequate for the real-time estimation of the orientation of a quadrotor.

  2. A unified Kalman filter

    Science.gov (United States)

    Stubberud, Allen R.

    2017-01-01

    When considering problems of linear sequential estimation, two versions of the Kalman filter, the continuous-time version and the discrete-time version, are often used. (A hybrid filter also exists.) In many applications in which the Kalman filter is used, the system to which the filter is applied is a linear continuous-time system, but the Kalman filter is implemented on a digital computer, a discrete-time device. The two general approaches for developing a discrete-time filter for implementation on a digital computer are: (1) approximate the continuous-time system by a discrete-time system (called discretization of the continuous-time system) and develop a filter for the discrete-time approximation; and (2) develop a continuous-time filter for the system and then discretize the continuous-time filter. Generally, the two discrete-time filters will be different, that is, it can be said that discretization and filter generation are not, in general, commutative operations. As a result, any relationship between the discrete-time and continuous-time versions of the filter for the same continuous-time system is often obfuscated. This is particularly true when an attempt is made to generate the continuous-time version of the Kalman filter through a simple limiting process (the sample period going to zero) applied to the discrete-time version. The correct result is, generally, not obtained. In a 1961 research report, Kalman showed that the continuous-time Kalman filter can be obtained from the discrete-time Kalman filter by taking limits as the sample period goes to zero if the white noise process for the continuous-time version is appropriately defined. Using this basic concept, a discrete-time Kalman filter can be developed for a continuous-time system as follows: (1) discretize the continuous-time system using Kalman's technique; and (2) develop a discrete-time Kalman filter for that discrete-time system. Kalman's results show that the discrete-time filter generated in

  3. Novel Kalman Filter Algorithm for Statistical Monitoring of Extensive Landscapes with Synoptic Sensor Data.

    Science.gov (United States)

    Czaplewski, Raymond L

    2015-09-17

    Wall-to-wall remotely sensed data are increasingly available to monitor landscape dynamics over large geographic areas. However, statistical monitoring programs that use post-stratification cannot fully utilize those sensor data. The Kalman filter (KF) is an alternative statistical estimator. I develop a new KF algorithm that is numerically robust with large numbers of study variables and auxiliary sensor variables. A National Forest Inventory (NFI) illustrates application within an official statistics program. Practical recommendations regarding remote sensing and statistical issues are offered. This algorithm has the potential to increase the value of synoptic sensor data for statistical monitoring of large geographic areas.

  4. Islanding Detection for Microgrid Based on Frequency Tracking Using Extended Kalman Filter Algorithm

    Directory of Open Access Journals (Sweden)

    Bin Li

    2014-01-01

    Full Text Available Islanding detection is essential for secure and reliable operation of microgrids. Considering the relationship between the power generation and the load in microgrids, frequency may vary with time when islanding occurs. As a common approach, frequency measurement is widely used to detect islanding condition. In this paper, a novel frequency calculation algorithm based on extended Kalman filter was proposed to track dynamic frequency of the microgrid. Taylor series expansion was introduced to solve nonlinear state equations. In addition, a typical microgrid model was built using MATLAB/SIMULINK. Simulation results demonstrated that the proposed algorithm achieved great stability and strong robustness in of tracking dynamic frequency.

  5. Kalman filtering with real-time applications

    CERN Document Server

    Chui, Charles K

    2017-01-01

    This new edition presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering. Over 100 exercises and problems with solutions help de...

  6. The Endogenous Kalman Filter

    OpenAIRE

    Brad Baxter; Liam Graham; Stephen Wright

    2007-01-01

    We relax the assumption of full information that underlies most dynamic general equilibrium models, and instead assume agents optimally form estimates of the states from an incomplete information set. We derive a version of the Kalman filter that is endogenous to agents' optimising decisions, and state conditions for its convergence. We show the (restrictive) conditions under which the endogenous Kalman filter will at least asymptotically reveal the true states. In general we show that incomp...

  7. Ensemble-Type Kalman Filter Algorithm conserving mass, total energy and enstrophy

    Science.gov (United States)

    Zeng, Yuefei; Janjic, Tijana; Ruckstuhl, Yvonne; Verlaan, Martin

    2017-04-01

    In a recent study (Zeng and Janjic 2016), we explored the effect on conservation properties of data assimilation using perfect model experiments with a 2D shallow water model preserving important properties of the true nonlinear flow. It was found that during the assimilation with the ensemble Kalman filter algorithm, the total energy of the analysis ensemble mean converges towards the nature run value with time. However, the enstrophy, divergence and energy spectra were strongly affected by the data assimilation settings. We tested the effects on the prediction depending on the type of error in the initial condition and showed that the accumulated noise during assimilation and the error of analysis are good indicators of the quality of the prediction. Having in mind that the conservation of both the kinetic energy and enstrophy by momentum advection schemes in the case of non-divergent flow prevents a systematic and unrealistic energy cascade towards the high wave numbers, we constructed the ensemble data assimilation algorithm that conserves both energy and enstrophy. This is done by extending QPEns (Janjic et al. 2014) to allow for nonlinear constraints using, instead of quadratic programming, the sequential quadratic programming algorithm. Experiments with the 2D shallow water model show similar RMSEs of the algorithm without constraints and the algorithm with only the total energy constrained. The algorithm which constraints enstrophy as well as energy and enstrophy during data assimilation showed smaller RMSE to the one without the constraint on enstrophy. Similar behavior can be seen in the energy spectrum where algorithms which include the constraint on enstrophy are closer to the true spectrum, in particular for wavelengths between 200 km and 1000 km. The enstrophy constraint resulted in a reduction of noise during data assimilation. Finally, the algorithm, with both energy and enstrophy constraint showed the smallest error growth during the two weeks

  8. Using frequency analysis to improve the precision of human body posture algorithms based on Kalman filters.

    Science.gov (United States)

    Olivares, Alberto; Górriz, J M; Ramírez, J; Olivares, G

    2016-05-01

    With the advent of miniaturized inertial sensors many systems have been developed within the last decade to study and analyze human motion and posture, specially in the medical field. Data measured by the sensors are usually processed by algorithms based on Kalman Filters in order to estimate the orientation of the body parts under study. These filters traditionally include fixed parameters, such as the process and observation noise variances, whose value has large influence in the overall performance. It has been demonstrated that the optimal value of these parameters differs considerably for different motion intensities. Therefore, in this work, we show that, by applying frequency analysis to determine motion intensity, and varying the formerly fixed parameters accordingly, the overall precision of orientation estimation algorithms can be improved, therefore providing physicians with reliable objective data they can use in their daily practice.

  9. A Framework of Finite-model Kalman Filter with Case Study: MVDP-FMKF Algorithm%A Framework of Finite-model Kalman Filter with Case Study:MVDP-FMKF Algorithm

    Institute of Scientific and Technical Information of China (English)

    FENG Bo; MA Hong-Bin; FU Meng-Yin; WANG Shun-Ting

    2013-01-01

    Kalman filtering techniques have been widely used in many applications,however,standard Kalman filters for linear Gaussian systems usually cannot work well or even diverge in the presence of large model uncertainty.In practical applications,it is expensive to have large number of high-cost experiments or even impossible to obtain an exact system model.Motivated by our previous pioneering work on finite-model adaptive control,a framework of finite-model Kalman filtering is introduced in this paper.This framework presumes that large model uncertainty may be restricted by a finite set of known models which can be very different from each other.Moreover,the number of known models in the set can be flexibly chosen so that the uncertain model may always be approximated by one of the known models,in other words,the large model uncertainty is "covered" by the "convex hull" of the known models.Within the presented framework according to the idea of adaptive switching via the minimizing vector distance principle,a simple finite-model Kalman filter,MVDP-FMKF,is mathematically formulated and illustrated by extensive simulations.An experiment of MEMS gyroscope drift has verified the effectiveness of the proposed algorithm,indicating that the mechanism of finite-model Kalman filter is useful and efficient in practical applications of Kalman filters,especially in inertial navigation systems.

  10. Enhanced phase unwrapping algorithm based on unscented Kalman filter, enhanced phase gradient estimator, and path-following strategy.

    Science.gov (United States)

    Xie, XianMing; Li, YingHui

    2014-06-20

    This paper presents an enhanced phase unwrapping algorithm by combining an unscented Kalman filter, an enhanced local phase gradient estimator based on an amended matrix pencil model, and a path-following strategy. This technology is able to accurately unwrap seriously noisy wrapped phase images by applying the unscented Kalman filter to simultaneously perform noise suppression and phase unwrapping along the path from the high-quality region to the low-quality region of the wrapped phase images. Results obtained with synthetic data and real data validate the effectiveness of the proposed method and show improved performance of this new algorithm with respect to some of the most used algorithms.

  11. Low-cost attitude determination system using an extended Kalman filter (EKF) algorithm

    Science.gov (United States)

    Esteves, Fernando M.; Nehmetallah, Georges; Abot, Jandro L.

    2016-05-01

    Attitude determination is one of the most important subsystems in spacecraft, satellite, or scientific balloon mission s, since it can be combined with actuators to provide rate stabilization and pointing accuracy for payloads. In this paper, a low-cost attitude determination system with a precision in the order of arc-seconds that uses low-cost commercial sensors is presented including a set of uncorrelated MEMS gyroscopes, two clinometers, and a magnetometer in a hierarchical manner. The faster and less precise sensors are updated by the slower, but more precise ones through an Extended Kalman Filter (EKF)-based data fusion algorithm. A revision of the EKF algorithm fundamentals and its implementation to the current application, are presented along with an analysis of sensors noise. Finally, the results from the data fusion algorithm implementation are discussed in detail.

  12. An optimal algorithm based on extended kalman filter and the data fusion for infrared touch overlay

    Science.gov (United States)

    Zhou, AiGuo; Cheng, ShuYi; Pan, Qiang Biao; Sun, Dong Yu

    2016-01-01

    Current infrared touch overlay has problems on the touch point recognition which bring some burrs on the touch trajectory. This paper uses the target tracking algorithm to improve the recognition and smoothness of infrared touch overlay. In order to deal with the nonlinear state estimate problem for touch point tracking, we use the extended Kalman filter in the target tracking algorithm. And we also use the data fusion algorithm to match the estimate value with the original target trajectory. The experimental results of the infrared touch overlay demonstrate that the proposed target tracking approach can improve the touch point recognition of the infrared touch overlay and achieve much smoother tracking trajectory than the existing tracking approach.

  13. Adaptable Iterative and Recursive Kalman Filter Schemes

    Science.gov (United States)

    Zanetti, Renato

    2014-01-01

    Nonlinear filters are often very computationally expensive and usually not suitable for real-time applications. Real-time navigation algorithms are typically based on linear estimators, such as the extended Kalman filter (EKF) and, to a much lesser extent, the unscented Kalman filter. The Iterated Kalman filter (IKF) and the Recursive Update Filter (RUF) are two algorithms that reduce the consequences of the linearization assumption of the EKF by performing N updates for each new measurement, where N is the number of recursions, a tuning parameter. This paper introduces an adaptable RUF algorithm to calculate N on the go, a similar technique can be used for the IKF as well.

  14. Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters

    CERN Document Server

    Hoteit, Ibrahim; Pham, Dinh-Tuan

    2011-01-01

    This paper investigates an approximation scheme of the optimal nonlinear Bayesian filter based on the Gaussian mixture representation of the state probability distribution function. The resulting filter is similar to the particle filter, but is different from it in that, the standard weight-type correction in the particle filter is complemented by the Kalman-type correction with the associated covariance matrices in the Gaussian mixture. We show that this filter is an algorithm in between the Kalman filter and the particle filter, and therefore is referred to as the particle Kalman filter (PKF). In the PKF, the solution of a nonlinear filtering problem is expressed as the weighted average of an "ensemble of Kalman filters" operating in parallel. Running an ensemble of Kalman filters is, however, computationally prohibitive for realistic atmospheric and oceanic data assimilation problems. For this reason, we consider the construction of the PKF through an "ensemble" of ensemble Kalman filters (EnKFs) instead, ...

  15. Multilevel Mixture Kalman Filter

    Directory of Open Access Journals (Sweden)

    Xiaodong Wang

    2004-11-01

    Full Text Available The mixture Kalman filter is a general sequential Monte Carlo technique for conditional linear dynamic systems. It generates samples of some indicator variables recursively based on sequential importance sampling (SIS and integrates out the linear and Gaussian state variables conditioned on these indicators. Due to the marginalization process, the complexity of the mixture Kalman filter is quite high if the dimension of the indicator sampling space is high. In this paper, we address this difficulty by developing a new Monte Carlo sampling scheme, namely, the multilevel mixture Kalman filter. The basic idea is to make use of the multilevel or hierarchical structure of the space from which the indicator variables take values. That is, we draw samples in a multilevel fashion, beginning with sampling from the highest-level sampling space and then draw samples from the associate subspace of the newly drawn samples in a lower-level sampling space, until reaching the desired sampling space. Such a multilevel sampling scheme can be used in conjunction with the delayed estimation method, such as the delayed-sample method, resulting in delayed multilevel mixture Kalman filter. Examples in wireless communication, specifically the coherent and noncoherent 16-QAM over flat-fading channels, are provided to demonstrate the performance of the proposed multilevel mixture Kalman filter.

  16. Inpatient studies of a Kalman-filter-based predictive pump shutoff algorithm.

    Science.gov (United States)

    Cameron, Fraser; Wilson, Darrell M; Buckingham, Bruce A; Arzumanyan, Hasmik; Clinton, Paula; Chase, H Peter; Lum, John; Maahs, David M; Calhoun, Peter M; Bequette, B Wayne

    2012-09-01

    An insulin pump shutoff system can prevent nocturnal hypoglycemia and is a first step on the pathway toward a closed-loop artificial pancreas. In previous pump shutoff studies using a voting algorithm and a 1 min continuous glucose monitor (CGM), 80% of induced hypoglycemic events were prevented. The pump shutoff algorithm used in previous studies was revised to a single Kalman filter to reduce complexity, incorporate CGMs with different sample times, handle sensor signal dropouts, and enforce safety constraints on the allowable pump shutoff time. Retrospective testing of the new algorithm on previous clinical data sets indicated that, for the four cases where the previous algorithm failed (minimum reference glucose less than 60 mg/dl), the mean suspension start time was 30 min earlier than the previous algorithm. Inpatient studies of the new algorithm have been conducted on 16 subjects. The algorithm prevented hypoglycemia in 73% of subjects. Suspension-induced hyperglycemia is not assessed, because this study forced excessive basal insulin infusion rates. The new algorithm functioned well and is flexible enough to handle variable sensor sample times and sensor dropouts. It also provides a framework for handling sensor signal attenuations, which can be challenging, particularly when they occur overnight. © 2012 Diabetes Technology Society.

  17. Bias aware Kalman filters

    DEFF Research Database (Denmark)

    Drecourt, J.-P.; Madsen, H.; Rosbjerg, Dan

    2006-01-01

    . The colored noise filter formulation is extended to correct both time correlated and uncorrelated model error components. A more stable version of the separate filter without feedback is presented. The filters are implemented in an ensemble framework using Latin hypercube sampling. The techniques...... are illustrated on a simple one-dimensional groundwater problem. The results show that the presented filters outperform the standard Kalman filter and that the implementations with bias feedback work in more general conditions than the implementations without feedback. 2005 Elsevier Ltd. All rights reserved....

  18. Multilevel ensemble Kalman filtering

    KAUST Repository

    Hoel, Haakon

    2016-01-08

    The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.

  19. Battery State-of-Charge and Parameter Estimation Algorithm Based on Kalman Filter

    DEFF Research Database (Denmark)

    Dragicevic, Tomislav; Sucic, Stjepan; Guerrero, Josep M.

    2013-01-01

    Electrochemical battery is the most widely used energy storage technology, finding its application in various devices ranging from low power consumer electronics to utility back-up power. All types of batteries show highly non-linear behaviour in terms of dependence of internal parameters...... on operating conditions, momentary replenishment and a number of past charge/discharge cycles. A good indicator for the quality of overall customer service in any battery based application is the availability and reliability of these informations, as they point out important runtime variables...... such as the actual state of charge (SOC) and state of health (SOH). Therefore, a modern battery management systems (BMSs) should incorporate functions that accommodate real time tracking of these nonlinearities. For that purpose, Kalman filter based algorithms emerged as a convenient solution due to their ability...

  20. Fast Visual Object Tracking Using Modified kalman and Particle Filtering Algorithms in the Presence of Occlusions

    Directory of Open Access Journals (Sweden)

    G.Mallikarjuna Rao

    2014-09-01

    Full Text Available In the present day real time applications of visual object tracking in surveillance, it has become extremely complex, time consuming and tricky to do the tracking when there are occlusions are present for small duration or for longer time and also when it is done in outdoor environments. In these conditions, the target to be tracked can be lost for few seconds and that should be tracked as soon as possible. As from the literature it is observed that particle filter can be able to track the target robustly in different kinds of background conditions, and it’s robust to partial occlusion. However, this tracking cannot recover from large proportion of occlusion and complete occlusion, to avoid this condition, we proposed two new algorithms (modified kalman and modified particle filter for fast tracking of objects in the presence of occlusions. We considered the complete occlusion of tracking object and the main objective is how fast the system is able to track the object after the occlusion is crossed. From the experimental results, it is observed that the proposed algorithms have shown good improvement in results compared to the traditional methods.

  1. Kalman plus weights: a time scale algorithm

    Science.gov (United States)

    Greenhall, C. A.

    2001-01-01

    KPW is a time scale algorithm that combines Kalman filtering with the basic time scale equation (BTSE). A single Kalman filter that estimates all clocks simultaneously is used to generate the BTSE frequency estimates, while the BTSE weights are inversely proportional to the white FM variances of the clocks. Results from simulated clock ensembles are compared to previous simulation results from other algorithms.

  2. 一种卡尔曼滤波自适应算法%An adaptive Algorithm on Kalman Filtering

    Institute of Scientific and Technical Information of China (English)

    黄波; 郑新星; 刘凤伟

    2012-01-01

    自适应滤波是指随着外部信号的变化,滤波器能够自我调节滤波参数,使得滤波器的某一性能指标达到最优。文章以卡尔曼滤波理论为基础,给出一种新的自适应卡尔曼滤波算法。%Adaptive-filtering means the filter could adjust filtration parameters by itself and make some performance index optimal when the external signals vary.This paper will give a new Kalman filter algorithm whose base is Kalman filter theory.

  3. Unscented Kalman Filter Algorithm for WiFi-PDR Integrated Indoor Positioning

    Directory of Open Access Journals (Sweden)

    CHEN GuoLiang

    2015-12-01

    Full Text Available Indoor positioning still faces lots of fundamental technical problems although it has been widely applied. A novel indoor positioning technology by using the smart phone with the assisting of the widely available and economically signals of WiFi is proposed. It also includes the principles and characteristics in indoor positioning. Firstly, improve the system's accuracy by fusing the WiFi fingerprinting positioning and PDR (ped estrian dead reckoning positioning with UKF (unscented Kalman filter. Secondly, improve the real-time performance by clustering the WiFi fingerprinting with k-means clustering algorithm. An investigation test was conducted at the indoor environment to learn about its performance on a HUAWEI P6-U06 smart phone. The result shows that compared to the pattern-matching system without clustering, an average reduction of 51% in the time cost can be obtained without degrading the positioning accuracy. When the state of personnel is walking, the average positioning error of WiFi is 7.76 m, the average positioning error of PDR is 4.57 m. After UKF fusing, the system's average positioning error is down to 1.24 m. It shows that the algorithm greatly improves the system's real-time and positioning accuracy.

  4. Tracking speckle displacement by double Kalman filtering

    Institute of Scientific and Technical Information of China (English)

    Donghui Li; Li Guo

    2006-01-01

    @@ A tracking technique using two sequentially-connected Kalman filter for tracking laser speckle displacement is presented. One Kalman filter tracks temporal speckle displacement, while another Kalman filter tracks spatial speckle displacement. The temporal Kalman filter provides a prior for the spatial Kalman filter, and the spatial Kalman filter provides measurements for the temporal Kalman filter. The contribution of a prior to estimations of the spatial Kalman filter is analyzed. An optical analysis system was set up to verify the double-Kalman-filter tracker's ability of tracking laser speckle's constant displacement.

  5. Multilevel ensemble Kalman filtering

    KAUST Repository

    Hoel, Hakon

    2016-06-14

    This work embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. The resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.

  6. Robust Kriged Kalman Filtering

    Energy Technology Data Exchange (ETDEWEB)

    Baingana, Brian; Dall' Anese, Emiliano; Mateos, Gonzalo; Giannakis, Georgios B.

    2015-11-11

    Although the kriged Kalman filter (KKF) has well-documented merits for prediction of spatial-temporal processes, its performance degrades in the presence of outliers due to anomalous events, or measurement equipment failures. This paper proposes a robust KKF model that explicitly accounts for presence of measurement outliers. Exploiting outlier sparsity, a novel l1-regularized estimator that jointly predicts the spatial-temporal process at unmonitored locations, while identifying measurement outliers is put forth. Numerical tests are conducted on a synthetic Internet protocol (IP) network, and real transformer load data. Test results corroborate the effectiveness of the novel estimator in joint spatial prediction and outlier identification.

  7. Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model

    Science.gov (United States)

    Barthélémy, S.; Ricci, S.; Pannekoucke, O.; Thual, O.; Malaterre, P. O.

    2013-06-01

    This study describes the emulation of an Ensemble Kalman Filter (EnKF) algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically with an ensemble method. In the case with assimilation with one observation point, where synthetical observations are generated by adding an error to a true state, the dynamic of the background error covariance functions is not straightforward and a numerical approach using an EnKF algorithm is prefered. First, those numerical experiments show that both background error variance and correlation length scale are reduced at the observation point. This reduction of variance and correlation length scale is propagated downstream by the dynamics of the model. Then, it is shown that the application of a Best Linear Unbiased Estimator (BLUE) algorithm using the background error covariance matrix converged from the EnKF algorithm, provides the same results as the EnKF but with a cheaper computational cost, thus allowing for the use of data assimilation in the context of real time flood forecasting. Moreover it was demonstrated that the reduction of background error correlation length scale and variance at the observation point depends on the error observation statistics. This feature is quantified by abacus built from linear regressions over a limited set of EnKF experiments. These abacus that describe the background error variance and the correlation length scale in the neighboring of the observation point combined with analytical expressions that describe the background error variance and the correlation length scale away from the observation point provide parametrized models for the variance and the correlation length scale. Using this

  8. Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model

    Directory of Open Access Journals (Sweden)

    S. Barthélémy

    2013-06-01

    Full Text Available This study describes the emulation of an Ensemble Kalman Filter (EnKF algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically with an ensemble method. In the case with assimilation with one observation point, where synthetical observations are generated by adding an error to a true state, the dynamic of the background error covariance functions is not straightforward and a numerical approach using an EnKF algorithm is prefered. First, those numerical experiments show that both background error variance and correlation length scale are reduced at the observation point. This reduction of variance and correlation length scale is propagated downstream by the dynamics of the model. Then, it is shown that the application of a Best Linear Unbiased Estimator (BLUE algorithm using the background error covariance matrix converged from the EnKF algorithm, provides the same results as the EnKF but with a cheaper computational cost, thus allowing for the use of data assimilation in the context of real time flood forecasting. Moreover it was demonstrated that the reduction of background error correlation length scale and variance at the observation point depends on the error observation statistics. This feature is quantified by abacus built from linear regressions over a limited set of EnKF experiments. These abacus that describe the background error variance and the correlation length scale in the neighboring of the observation point combined with analytical expressions that describe the background error variance and the correlation length scale away from the observation point provide parametrized models for the variance and the correlation length

  9. Distributed Kalman Filter via Gaussian Belief Propagation

    CERN Document Server

    Bickson, Danny; Dolev, Danny

    2008-01-01

    Recent result shows how to compute distributively and efficiently the linear MMSE for the multiuser detection problem, using the Gaussian BP algorithm. In the current work, we extend this construction, and show that operating this algorithm twice on the matching inputs, has several interesting interpretations. First, we show equivalence to computing one iteration of the Kalman filter. Second, we show that the Kalman filter is a special case of the Gaussian information bottleneck algorithm, when the weight parameter $\\beta = 1$. Third, we discuss the relation to the Affine-scaling interior-point method and show it is a special case of Kalman filter. Besides of the theoretical interest of this linking estimation, compression/clustering and optimization, we allow a single distributed implementation of those algorithms, which is a highly practical and important task in sensor and mobile ad-hoc networks. Application to numerous problem domains includes collaborative signal processing and distributed allocation of ...

  10. Deterministic Kalman filtering in a behavioral framework

    NARCIS (Netherlands)

    Fagnani, F; Willems, JC

    1997-01-01

    The purpose of this paper is to obtain a deterministic version of the Kalman filtering equations. We will use a behavioral description of the plant, specifically, an image representation. The resulting algorithm requires a matrix spectral factorization. We also show that the filter can be implemente

  11. Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters*

    KAUST Repository

    Hoteit, Ibrahim

    2012-02-01

    This paper investigates an approximation scheme of the optimal nonlinear Bayesian filter based on the Gaussian mixture representation of the state probability distribution function. The resulting filter is similar to the particle filter, but is different from it in that the standard weight-type correction in the particle filter is complemented by the Kalman-type correction with the associated covariance matrices in the Gaussian mixture. The authors show that this filter is an algorithm in between the Kalman filter and the particle filter, and therefore is referred to as the particle Kalman filter (PKF). In the PKF, the solution of a nonlinear filtering problem is expressed as the weighted average of an “ensemble of Kalman filters” operating in parallel. Running an ensemble of Kalman filters is, however, computationally prohibitive for realistic atmospheric and oceanic data assimilation problems. For this reason, the authors consider the construction of the PKF through an “ensemble” of ensemble Kalman filters (EnKFs) instead, and call the implementation the particle EnKF (PEnKF). It is shown that different types of the EnKFs can be considered as special cases of the PEnKF. Similar to the situation in the particle filter, the authors also introduce a resampling step to the PEnKF in order to reduce the risk of weights collapse and improve the performance of the filter. Numerical experiments with the strongly nonlinear Lorenz-96 model are presented and discussed.

  12. Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

    Science.gov (United States)

    Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank

    2012-01-01

    This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.

  13. A search algorithm to meta-optimize the parameters for an extended Kalman filter to improve classification on hyper-temporal images

    CSIR Research Space (South Africa)

    Salmon, BP

    2012-07-01

    Full Text Available In this paper the Bias Variance Search Algorithm is proposed as an algorithm to optimize a candidate set of initial parameters for an Extended Kalman filter (EKF). The search algorithm operates on a Bias Variance Equilibrium Point criterion...

  14. Q-Method Extended Kalman Filter

    Science.gov (United States)

    Zanetti, Renato; Ainscough, Thomas; Christian, John; Spanos, Pol D.

    2012-01-01

    A new algorithm is proposed that smoothly integrates non-linear estimation of the attitude quaternion using Davenport s q-method and estimation of non-attitude states through an extended Kalman filter. The new method is compared to a similar existing algorithm showing its similarities and differences. The validity of the proposed approach is confirmed through numerical simulations.

  15. Reduced Kalman Filters for Clock Ensembles

    Science.gov (United States)

    Greenhall, Charles A.

    2011-01-01

    This paper summarizes the author's work ontimescales based on Kalman filters that act upon the clock comparisons. The natural Kalman timescale algorithm tends to optimize long-term timescale stability at the expense of short-term stability. By subjecting each post-measurement error covariance matrix to a non-transparent reduction operation, one obtains corrected clocks with improved short-term stability and little sacrifice of long-term stability.

  16. Design of jitter compensation algorithm for robot vision based on optical flow and Kalman filter.

    Science.gov (United States)

    Wang, B R; Jin, Y L; Shao, D L; Xu, Y

    2014-01-01

    Image jitters occur in the video of the autonomous robot moving on bricks road, which will reduce robot operation precision based on vision. In order to compensate the image jitters, the affine transformation kinematics were established for obtaining the six image motion parameters. The feature point pair detecting method was designed based on Eigen-value of the feature windows gradient matrix, and the motion parameters equation was solved using the least square method and the matching point pairs got based on the optical flow. The condition number of coefficient matrix was proposed to quantificationally analyse the effect of matching errors on parameters solving errors. Kalman filter was adopted to smooth image motion parameters. Computing cases show that more point pairs are beneficial for getting more precise motion parameters. The integrated jitters compensation software was developed with feature points detecting in subwindow. And practical experiments were conducted on two mobile robots. Results show that the compensation costing time is less than frame sample time and Kalman filter is valid for robot vision jitters compensation.

  17. Design of Jitter Compensation Algorithm for Robot Vision Based on Optical Flow and Kalman Filter

    Directory of Open Access Journals (Sweden)

    B. R. Wang

    2014-01-01

    Full Text Available Image jitters occur in the video of the autonomous robot moving on bricks road, which will reduce robot operation precision based on vision. In order to compensate the image jitters, the affine transformation kinematics were established for obtaining the six image motion parameters. The feature point pair detecting method was designed based on Eigen-value of the feature windows gradient matrix, and the motion parameters equation was solved using the least square method and the matching point pairs got based on the optical flow. The condition number of coefficient matrix was proposed to quantificationally analyse the effect of matching errors on parameters solving errors. Kalman filter was adopted to smooth image motion parameters. Computing cases show that more point pairs are beneficial for getting more precise motion parameters. The integrated jitters compensation software was developed with feature points detecting in subwindow. And practical experiments were conducted on two mobile robots. Results show that the compensation costing time is less than frame sample time and Kalman filter is valid for robot vision jitters compensation.

  18. Kalman filtering implementation with Matlab

    OpenAIRE

    Kleinbauer, Rachel

    2004-01-01

    1960 und 1961 veröffentlichte Rudolf Emil Kalmen seine Arbeiten über einen rekursiven prädiktiven Filter, der auf dem Gebrauch von rekursiven Algorithmen basiert. Damit revolutionierte er das Feld der Schätzverfahren. Seitdem ist der sogenannte Kalman Filter Gegenstand ausführlicher Forschung und findet bis heute Anwendung in zahlreichen Gebieten. Der Kalman Filter schätzt den Zustand eines dynamischen Systems, auch wenn die exakte Form dieses Systems unbekannt ist. Der Filter ist sehr lei...

  19. A Comprehensive Motion Estimation Technique for the Improvement of EIS Methods Based on the SURF Algorithm and Kalman Filter.

    Science.gov (United States)

    Cheng, Xuemin; Hao, Qun; Xie, Mengdi

    2016-04-07

    Video stabilization is an important technology for removing undesired motion in videos. This paper presents a comprehensive motion estimation method for electronic image stabilization techniques, integrating the speeded up robust features (SURF) algorithm, modified random sample consensus (RANSAC), and the Kalman filter, and also taking camera scaling and conventional camera translation and rotation into full consideration. Using SURF in sub-pixel space, feature points were located and then matched. The false matched points were removed by modified RANSAC. Global motion was estimated by using the feature points and modified cascading parameters, which reduced the accumulated errors in a series of frames and improved the peak signal to noise ratio (PSNR) by 8.2 dB. A specific Kalman filter model was established by considering the movement and scaling of scenes. Finally, video stabilization was achieved with filtered motion parameters using the modified adjacent frame compensation. The experimental results proved that the target images were stabilized even when the vibrating amplitudes of the video become increasingly large.

  20. Heading Estimation with Real-time Compensation Based on Kalman Filter Algorithm for an Indoor Positioning System

    Directory of Open Access Journals (Sweden)

    Xin Li

    2016-06-01

    Full Text Available The problem of heading drift error using only low cost Micro-Electro-Mechanical (MEMS Inertial-Measurement-Unit (IMU has not been well solved. In this paper, a heading estimation method with real-time compensation based on Kalman filter has been proposed, abbreviated as KHD. For the KHD method, a unified heading error model is established for various predictable errors in magnetic compass for pedestrian navigation, and an effective method for solving the model parameters is proposed in the indoor environment with regular structure. In addition, error model parameters are solved by Kalman filtering algorithm with building geometry information in order to achieve real-time heading compensation. The experimental results show that the KHD method can not only effectively correct the original heading information, but also effectively inhibit the accumulation effect of positioning errors. The performance observed in a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI building on the China University of Mining and Technology (CUMT campus confirms that apply KHD method to PDR(Pedestrian Dead Reckoning algorithm can reliably achieve meter-level positioning using a low cost MEMS IMU only.

  1. A distributed Kalman filter with global covariance

    NARCIS (Netherlands)

    Sijs, J.; Lazar, M.

    2011-01-01

    Most distributed Kalman filtering (DKF) algorithms for sensor networks calculate a local estimate of the global state-vector in each node. An important challenge within distributed estimation is that all sensors in the network contribute to the local estimate in each node. In this paper, a novel DKF

  2. A class of quaternion Kalman filters.

    Science.gov (United States)

    Jahanchahi, Cyrus; Mandic, Danilo P

    2014-03-01

    The existing Kalman filters for quaternion-valued signals do not operate fully in the quaternion domain, and are combined with the real Kalman filter to enable the tracking in 3-D spaces. Using the recently introduced HR-calculus, we develop the fully quaternion-valued Kalman filter (QKF) and quaternion-extended Kalman filter (QEKF), allowing for the tracking of 3-D and 4-D signals directly in the quaternion domain. To consider the second-order noncircularity of signals, we employ the recently developed augmented quaternion statistics to derive the widely linear QKF (WL-QKF) and widely linear QEKF (WL-QEKF). To reduce computational requirements of the widely linear algorithms, their efficient implementation are proposed and it is shown that the quaternion widely linear model can be simplified when processing 3-D data, further reducing the computational requirements. Simulations using both synthetic and real-world circular and noncircular signals illustrate the advantages offered by widely linear over strictly linear quaternion Kalman filters.

  3. On sequential observation processing in localized ensemble Kalman filters

    OpenAIRE

    Nerger, Lars

    2014-01-01

    The different variants of current ensemble square-root Kalman filters assimilate either all observations at once or perform a sequence in which batches of observations or each single observation is assimilated. The sequential observation processing is used in filter algorithms like the ensemble adjustment Kalman filter (EAKF) and the ensemble square-root filter (EnSRF) and can result in computationally efficient algorithms because matrix inversions in the observation space are reduced to the ...

  4. Particle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters

    KAUST Repository

    Hoteit, Ibrahim

    2010-09-19

    Optimal nonlinear filtering consists of sequentially determining the conditional probability distribution functions (pdf) of the system state, given the information of the dynamical and measurement processes and the previous measurements. Once the pdfs are obtained, one can determine different estimates, for instance, the minimum variance estimate, or the maximum a posteriori estimate, of the system state. It can be shown that, many filters, including the Kalman filter (KF) and the particle filter (PF), can be derived based on this sequential Bayesian estimation framework. In this contribution, we present a Gaussian mixture‐based framework, called the particle Kalman filter (PKF), and discuss how the different EnKF methods can be derived as simplified variants of the PKF. We also discuss approaches to reducing the computational burden of the PKF in order to make it suitable for complex geosciences applications. We use the strongly nonlinear Lorenz‐96 model to illustrate the performance of the PKF.

  5. RAPID TRANSFER ALIGNMENT USING FEDERATED KALMAN FILTER

    Institute of Scientific and Technical Information of China (English)

    GUDong-qing; QINYong-yuan; PENGRong; LIXin

    2005-01-01

    The dimension number of the centralized Kalman filter (CKF) for the rapid transfer alignment (TA) is as high as 21 if the aircraft wing flexure motion is considered in the rapid TA. The 21-dimensional CKF brings the calculation burden on the computer and the difficulty to meet a high filtering updating rate desired by rapid TA. The federated Kalman filter (FKF) for the rapid TA is proposed to solve the dilemma. The structure and the algorithm of the FKF, which can perform parallel computation and has less calculation burden, are designed.The wing flexure motion is modeled, and then the 12-order velocity matching local filter and the 15-order attitud ematching local filter are devised. Simulation results show that the proposed EKE for the rapid TA almost has the same performance as the CKF. Thus the calculation burden of the proposed FKF for the rapid TA is markedly decreased.

  6. Adaptive Federal Kalman Filtering for SINS/GPS Integrated System

    Institute of Scientific and Technical Information of China (English)

    杨勇; 缪玲娟

    2003-01-01

    A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.

  7. An application of Chen system for secure chaotic communication based on extended Kalman filter and multi-shift cipher algorithm

    Science.gov (United States)

    Fallahi, Kia; Raoufi, Reza; Khoshbin, Hossein

    2008-07-01

    In recent years chaotic secure communication and chaos synchronization have received ever increasing attention. In this paper a chaotic communication method using extended Kalman filter is presented. The chaotic synchronization is implemented by EKF design in the presence of channel additive noise and processing noise. Encoding chaotic communication is used to achieve a satisfactory, typical secure communication scheme. In the proposed system, a multi-shift cipher algorithm is also used to enhance the security and the key cipher is chosen as one of the chaos states. The key estimate is employed to recover the primary data. To illustrate the effectiveness of the proposed scheme, a numerical example based on Chen dynamical system is presented and the results are compared to two other chaotic systems.

  8. A mollified Ensemble Kalman filter

    CERN Document Server

    Bergemann, Kay

    2010-01-01

    It is well recognized that discontinuous analysis increments of sequential data assimilation systems, such as ensemble Kalman filters, might lead to spurious high frequency adjustment processes in the model dynamics. Various methods have been devised to continuously spread out the analysis increments over a fixed time interval centered about analysis time. Among these techniques are nudging and incremental analysis updates (IAU). Here we propose another alternative, which may be viewed as a hybrid of nudging and IAU and which arises naturally from a recently proposed continuous formulation of the ensemble Kalman analysis step. A new slow-fast extension of the popular Lorenz-96 model is introduced to demonstrate the properties of the proposed mollified ensemble Kalman filter.

  9. A Kalman filter-based short baseline RTK algorithm for single-frequency combination of GPS and BDS.

    Science.gov (United States)

    Zhao, Sihao; Cui, Xiaowei; Guan, Feng; Lu, Mingquan

    2014-08-20

    The emerging Global Navigation Satellite Systems (GNSS) including the BeiDou Navigation Satellite System (BDS) offer more visible satellites for positioning users. To employ those new satellites in a real-time kinematic (RTK) algorithm to enhance positioning precision and availability, a data processing model for the dual constellation of GPS and BDS is proposed and analyzed. A Kalman filter-based algorithm is developed to estimate the float ambiguities for short baseline scenarios. The entire work process of the high-precision algorithm based on the proposed model is deeply investigated in detail. The model is validated with real GPS and BDS data recorded from one zero and two short baseline experiments. Results show that the proposed algorithm can generate fixed baseline output with the same precision level as that of either a single GPS or BDS RTK algorithm. The significantly improved fixed rate and time to first fix of the proposed method demonstrates a better availability and effectiveness on processing multi-GNSSs.

  10. A Kalman Filter-Based Short Baseline RTK Algorithm for Single-Frequency Combination of GPS and BDS

    Directory of Open Access Journals (Sweden)

    Sihao Zhao

    2014-08-01

    Full Text Available The emerging Global Navigation Satellite Systems (GNSS including the BeiDou Navigation Satellite System (BDS offer more visible satellites for positioning users. To employ those new satellites in a real-time kinematic (RTK algorithm to enhance positioning precision and availability, a data processing model for the dual constellation of GPS and BDS is proposed and analyzed. A Kalman filter-based algorithm is developed to estimate the float ambiguities for short baseline scenarios. The entire work process of the high-precision algorithm based on the proposed model is deeply investigated in detail. The model is validated with real GPS and BDS data recorded from one zero and two short baseline experiments. Results show that the proposed algorithm can generate fixed baseline output with the same precision level as that of either a single GPS or BDS RTK algorithm. The significantly improved fixed rate and time to first fix of the proposed method demonstrates a better availability and effectiveness on processing multi-GNSSs.

  11. Indirect Kalman Filter in Mobile Robot Application

    Directory of Open Access Journals (Sweden)

    Surachai Panich

    2010-01-01

    Full Text Available Problem statement: The most successful applications of Kalman filtering are to linearize about some nominal trajectory in state space that does not depend on the measurement data. The resulting filter is usually referred to as simply a linearized Kalman filter. Approach: This study introduced mainly indirect Kalman filter to estimate robot’s position. A developed differential encoder system integrated accelerometer is experimental tested in square shape. Results: Experimental results confirmed that indirect Kalman filter improves the accuracy and confidence of position estimation. Conclusion: In summary, we concluded that indirect Kalman filter has good potential to reduce error of measurement data.

  12. Star-sensor-based predictive Kalman filter for satelliteattitude estimation

    Institute of Scientific and Technical Information of China (English)

    林玉荣; 邓正隆

    2002-01-01

    A real-time attitude estimation algorithm, namely the predictive Kalman filter, is presented. This algorithm can accurately estimate the three-axis attitude of a satellite using only star sensor measurements. The implementation of the filter includes two steps: first, predicting the torque modeling error, and then estimating the attitude. Simulation results indicate that the predictive Kalman filter provides robust performance in the presence of both significant errors in the assumed model and in the initial conditions.

  13. Uncertainty Representation and Interpretation in Model-based Prognostics Algorithms based on Kalman Filter Estimation

    Data.gov (United States)

    National Aeronautics and Space Administration — This article discusses several aspects of uncertainty represen- tation and management for model-based prognostics method- ologies based on our experience with Kalman...

  14. Kalman filter-based algorithms for monitoring the ionosphere and plasmasphere with GPS in near-real time

    Science.gov (United States)

    Anghel, Adela; Carrano, Charles; Komjathy, Attila; Astilean, Adina; Letia, Tiberiu

    2009-01-01

    Data collected from a GPS receiver located at low latitudes in the American sector are used to investigate the performance of the WinTEC algorithm [Anghel et al., 2008a, Kalman filter-based algorithm for near realtime monitoring of the ionosphere using dual frequency GPS data. GPS Solutions, accepted for publication; for different ionospheric modeling techniques: the single-shell linear, quadratic, and cubic approaches, and the multi-shell linear approach. Our results indicate that the quadratic and cubic approaches perform much better than the single-shell and multi-shell linear approaches in terms of post-fit residuals. The performance of the algorithm for the cubic approach is then further tested by comparing the vertical TEC predicted by WinTEC and USTEC [Spencer et al., 2004. Ionospheric data assimilation methods for geodetic applications. In: Proceedings of IEEE PLANS, Monterey, CA, 26-29 April, pp. 510-517] at five North American stations. In addition, since the GPS-derived total electron content (TEC) contains contributions from both ionospheric and plasmaspheric sections of the GPS ray paths, in an effort to improve the accuracy of the TEC retrievals, a new data assimilation module that uses background information from an empirical plasmaspheric model [Gallagher et al., 1988. An empirical model of the Earth's plasmasphere. Advances in Space Research 8, (8)15-(8)24] has been incorporated into the WinTEC algorithm. The new Kalman filter-based algorithm estimates both the ionospheric and plasmaspheric electron contents, the combined satellite and receiver biases, and the estimation error covariance matrix, in a single-site or network solution. To evaluate the effect of the plasmaspheric component on the estimated biases and total TEC and to assess the performance of the newly developed algorithm, we compare the WinTEC results, with and without the plasmaspheric term included, at three GPS receivers located at different latitudes in the American sector, during

  15. Integrated WiFi/PDR/Smartphone Using an Adaptive System Noise Extended Kalman Filter Algorithm for Indoor Localization

    Directory of Open Access Journals (Sweden)

    Xin Li

    2016-02-01

    Full Text Available Wireless signal strength is susceptible to the phenomena of interference, jumping, and instability, which often appear in the positioning results based on Wi-Fi field strength fingerprint database technology for indoor positioning. Therefore, a Wi-Fi and PDR (pedestrian dead reckoning real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement (straight or turning, which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem. Wi-Fi fingerprint matching typically requires a quite high computational burden: To reduce the computational complexity of this step, the affinity propagation clustering algorithm is adopted to cluster the fingerprint database and integrate the information of the position domain and signal domain of respective points. An experiment performed in a fourth-floor corridor at the School of Environment and Spatial Informatics, China University of Mining and Technology, shows that the traverse points of the clustered positioning system decrease by 65%–80%, which greatly improves the time efficiency. In terms of positioning accuracy, the average error is 4.09 m through the Wi-Fi positioning method. However, the positioning error can be reduced to 2.32 m after integration of the PDR algorithm with the adaptive noise extended Kalman filter (EKF.

  16. Liquid Level Estimation in Dynamic Condition using Kalman Filter

    Directory of Open Access Journals (Sweden)

    Sagar Kapale

    2016-08-01

    Full Text Available The aim of this paper is to estimate true liquid level of tank from noisy measurements due to dynamic conditions using kalman filter algorithm. We proposed kalman filter based approach to reduce noise in liquid level measurement system due to effect like sloshing. The function of kalman filter is to reduce error in liquid level measurement that produced from sensor resulting from effect like sloshing in dynamic environment. A prototype model was constructed and placed in dynamic condition, level data was acquired using ultrasonic sensor to verify the effectiveness of kalman filter. The tabulated data are shown for comparison of accuracy and error analysis between both measurements with Kalman filter and statistical averaging filter. After several test with different liquid levels and analysis of the recorded data, the technique shows the usefulness in liquid level measurement application in dynamic condition.

  17. An Adaptive Compensation Algorithm for Temperature Drift of Micro-Electro-Mechanical Systems Gyroscopes Using a Strong Tracking Kalman Filter

    Directory of Open Access Journals (Sweden)

    Yibo Feng

    2015-05-01

    Full Text Available We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF, the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to −2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.

  18. An adaptive compensation algorithm for temperature drift of micro-electro-mechanical systems gyroscopes using a strong tracking Kalman filter.

    Science.gov (United States)

    Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan

    2015-05-13

    We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.

  19. An Improved PDR/Magnetometer/Floor Map Integration Algorithm for Ubiquitous Positioning Using the Adaptive Unscented Kalman Filter

    Directory of Open Access Journals (Sweden)

    Jian Wang

    2015-11-01

    Full Text Available In this paper, a scheme is presented for fusing a foot-mounted Inertial Measurement Unit (IMU and a floor map to provide ubiquitous positioning in a number of settings, such as in a supermarket as a shopping guide, in a fire emergency service for navigation, or with a hospital patient to be tracked. First, several Zero-Velocity Detection (ZDET algorithms are compared and discussed when used in the static detection of a pedestrian. By introducing information on the Zero Velocity of the pedestrian, fused with a magnetometer measurement, an improved Pedestrian Dead Reckoning (PDR model is developed to constrain the accumulating errors associated with the PDR positioning. Second, a Correlation Matching Algorithm based on map projection (CMAP is presented, and a zone division of a floor map is demonstrated for fusion of the PDR algorithm. Finally, in order to use the dynamic characteristics of a pedestrian’s trajectory, the Adaptive Unscented Kalman Filter (A-UKF is applied to tightly integrate the IMU, magnetometers and floor map for ubiquitous positioning. The results of a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI building on the China University of Mining and Technology (CUMT campus confirm that the proposed scheme can reliably achieve meter-level positioning.

  20. Research on SINS/GPS Integrated Navigation Kalman Filter Algorithm%SINS/GPS组合导航卡尔曼滤波算法研究

    Institute of Scientific and Technical Information of China (English)

    牛强军; 张潮; 苏登辉

    2015-01-01

    Aiming at that single use of any kind of navigation equipment will not meet requirement of airborne fire control system and flight system, deduce a new Kalman filter algorithm of SINS/GPS integrated navigation. Introduce albinism processing of colored noise into Kalman filter, design a dynamic vehicle integrated navigation test system, put forwards the steps of Kalman filter algorithm based on colored noise albinism, use dynamic vehicle SINS/GPS integrated navigation system test data analysis to validate correctness and rationality of algorithm. The analyse results show that, Kalman filter which based on colored noise albinism make up for the deficiency of traditional Kalman filter, and improve the accuracy of navigation result.%针对单独使用某一种的导航设备都无法满足机载火控系统和飞行系统要求的问题,推导出 SINS/GPS 组合导航中的一种新的卡尔曼滤波算法.将有色噪声的白化处理引入到卡尔曼滤波器,设计了一套动态车载组合导航试验系统,给出了基于有色噪声白化的卡尔曼滤波器算法的具体步骤,以动态车载 SINS/GPS 组合导航系统试验的数据分析验证了此算法的正确性和合理性.分析结果表明:基于有色噪声白化的卡尔曼滤波器可以很好地解决有色噪声的影响,弥补了传统卡尔曼滤波器的不足,提高了导航结果的精确度.

  1. A New Quaternion-Based Kalman Filter for Real-Time Attitude Estimation Using the Two-Step Geometrically-Intuitive Correction Algorithm.

    Science.gov (United States)

    Feng, Kaiqiang; Li, Jie; Zhang, Xiaoming; Shen, Chong; Bi, Yu; Zheng, Tao; Liu, Jun

    2017-09-19

    In order to reduce the computational complexity, and improve the pitch/roll estimation accuracy of the low-cost attitude heading reference system (AHRS) under conditions of magnetic-distortion, a novel linear Kalman filter, suitable for nonlinear attitude estimation, is proposed in this paper. The new algorithm is the combination of two-step geometrically-intuitive correction (TGIC) and the Kalman filter. In the proposed algorithm, the sequential two-step geometrically-intuitive correction scheme is used to make the current estimation of pitch/roll immune to magnetic distortion. Meanwhile, the TGIC produces a computed quaternion input for the Kalman filter, which avoids the linearization error of measurement equations and reduces the computational complexity. Several experiments have been carried out to validate the performance of the filter design. The results demonstrate that the mean time consumption and the root mean square error (RMSE) of pitch/roll estimation under magnetic disturbances are reduced by 45.9% and 33.8%, respectively, when compared with a standard filter. In addition, the proposed filter is applicable for attitude estimation under various dynamic conditions.

  2. Data assimilation in the early phase: Kalman filtering RIMPUFF

    DEFF Research Database (Denmark)

    Astrup, P.; Turcanu, C.; Puch, R.O.;

    2004-01-01

    of RODOS (Realtime Online DecisiOn Support system for nuclear emergencies) – has been developed. It is built on the Kalman filtering algorithm and it assimilates 10-minute averaged gamma dose rates measured atground level stations. Since the gamma rates are non-linear functions of the state vector...... variables, the applied Kalman filter is the so-called Extended Kalman filter. In more ways the implementation is non standard: 1) the number of state vectorvariables varies with time, and 2) the state vector variables are prediction updated with 1-minute time steps but only Kalman filtered every 10 minutes......, and this based on time averaged measurements. Given reasonable conditions, i.e. a spatially densedistribution of gamma monitors and a realistic wind field, the developed ADUM module is found to be able to enhance the prediction of the gamma dose field. Based on some of the Kalman filtering parameters, another...

  3. Mixtures of skewed Kalman filters

    KAUST Repository

    Kim, Hyoungmoon

    2014-01-01

    Normal state-space models are prevalent, but to increase the applicability of the Kalman filter, we propose mixtures of skewed, and extended skewed, Kalman filters. To do so, the closed skew-normal distribution is extended to a scale mixture class of closed skew-normal distributions. Some basic properties are derived and a class of closed skew. t distributions is obtained. Our suggested family of distributions is skewed and has heavy tails too, so it is appropriate for robust analysis. Our proposed special sequential Monte Carlo methods use a random mixture of the closed skew-normal distributions to approximate a target distribution. Hence it is possible to handle skewed and heavy tailed data simultaneously. These methods are illustrated with numerical experiments. © 2013 Elsevier Inc.

  4. The Reduced Rank of Ensemble Kalman Filter to Estimate the Temperature of Non Isothermal Continue Stirred Tank Reactor

    OpenAIRE

    Erna Apriliani; Dieky Adzkiya; Arief Baihaqi

    2011-01-01

    Kalman filter is an algorithm to estimate the state variable of dynamical stochastic system. The square root ensemble Kalman filter is an modification of Kalman filter. The square root ensemble Kalman filter is proposed to keep the computational stability and reduce the computational time. In this paper we study the efficiency of the reduced rank ensemble Kalman filter. We apply this algorithm to the non isothermal continue stirred tank reactor problem. We decompose the covariance of the ense...

  5. Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System

    Directory of Open Access Journals (Sweden)

    T. O. Ting

    2014-01-01

    Full Text Available In this work, a state-space battery model is derived mathematically to estimate the state-of-charge (SoC of a battery system. Subsequently, Kalman filter (KF is applied to predict the dynamical behavior of the battery model. Results show an accurate prediction as the accumulated error, in terms of root-mean-square (RMS, is a very small value. From this work, it is found that different sets of Q and R values (KF’s parameters can be applied for better performance and hence lower RMS error. This is the motivation for the application of a metaheuristic algorithm. Hence, the result is further improved by applying a genetic algorithm (GA to tune Q and R parameters of the KF. In an online application, a GA can be applied to obtain the optimal parameters of the KF before its application to a real plant (system. This simply means that the instantaneous response of the KF is not affected by the time consuming GA as this approach is applied only once to obtain the optimal parameters. The relevant workable MATLAB source codes are given in the appendix to ease future work and analysis in this area.

  6. Tuning of Kalman filter parameters via genetic algorithm for state-of-charge estimation in battery management system.

    Science.gov (United States)

    Ting, T O; Man, Ka Lok; Lim, Eng Gee; Leach, Mark

    2014-01-01

    In this work, a state-space battery model is derived mathematically to estimate the state-of-charge (SoC) of a battery system. Subsequently, Kalman filter (KF) is applied to predict the dynamical behavior of the battery model. Results show an accurate prediction as the accumulated error, in terms of root-mean-square (RMS), is a very small value. From this work, it is found that different sets of Q and R values (KF's parameters) can be applied for better performance and hence lower RMS error. This is the motivation for the application of a metaheuristic algorithm. Hence, the result is further improved by applying a genetic algorithm (GA) to tune Q and R parameters of the KF. In an online application, a GA can be applied to obtain the optimal parameters of the KF before its application to a real plant (system). This simply means that the instantaneous response of the KF is not affected by the time consuming GA as this approach is applied only once to obtain the optimal parameters. The relevant workable MATLAB source codes are given in the appendix to ease future work and analysis in this area.

  7. Implementation of Kalman Filter with Python Language

    CERN Document Server

    Laaraiedh, Mohamed

    2012-01-01

    In this paper, we investigate the implementation of a Python code for a Kalman Filter using the Numpy package. A Kalman Filtering is carried out in two steps: Prediction and Update. Each step is investigated and coded as a function with matrix input and output. These different functions are explained and an example of a Kalman Filter application for the localization of mobile in wireless networks is given.

  8. Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization.

    Science.gov (United States)

    Chen, Guoliang; Meng, Xiaolin; Wang, Yunjia; Zhang, Yanzhe; Tian, Peng; Yang, Huachao

    2015-09-23

    Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone's acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals.

  9. Using an Extended Kalman Filter Learning Algorithm for Feed-Forward Neural Networks to Describe Tracer Correlations

    Science.gov (United States)

    Lary, David J.; Mussa, Yussuf

    2004-01-01

    In this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.

  10. Using an extended Kalman filter learning algorithm for feed-forward neural networks to describe tracer correlations

    Directory of Open Access Journals (Sweden)

    D. J. Lary

    2004-06-01

    Full Text Available In this study a new extended Kalman filter (EKF learning algorithm for feed-forward neural networks (FFN is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.. The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.

  11. Stochastic modelling of aquifer level temporal fluctuations using the Kalman filter adaptation algorithm and an autoregressive exogenous variable model

    Science.gov (United States)

    Varouchakis, Emmanouil

    2017-04-01

    Reliable temporal modelling of groundwater level is significant for efficient water resources management in hydrological basins and for the prevention of possible desertification effects. In this work we propose a stochastic data driven approach of temporal monitoring and prediction that can incorporate auxiliary information. More specifically, we model the temporal (mean annual and biannual) variation of groundwater level by means of a discrete time autoregressive exogenous variable model (ARX model). The ARX model parameters and its predictions are estimated by means of the Kalman filter adaptation algorithm (KFAA). KFAA is suitable for sparsely monitored basins that do not allow for an independent estimation of the ARX model parameters. Three new modified versions of the original form of the ARX model are proposed and investigated: the first considers a larger time scale, the second a larger time delay in terms of the groundwater level input and the third considers the groundwater level difference between the last two hydrological years, which is incorporated in the model as a third input variable. We apply KFAA to time series of groundwater level values from Mires basin in the island of Crete. In addition to precipitation measurements, we use pumping data as exogenous variables. We calibrate the ARX model based on the groundwater level for the years 1981 to 2006 and use it to successfully predict the mean annual and biannual groundwater level for recent years (2007-2010).

  12. Estimation of attitudes from a low-cost miniaturized inertial platform using Kalman Filter-based sensor fusion algorithm

    Indian Academy of Sciences (India)

    N Shantha Kumar; T Jann

    2004-04-01

    Due to costs, size and mass, commercially available inertial navigation systems are not suitable for small, autonomous flying vehicles like ALEX and other UAVs. In contrast, by using modern MEMS (or of similar class) sensors, hardware costs, size and mass can be reduced substantially. However, low-cost sensors often suffer from inaccuracy and are influenced greatly by temperature variation. In this work, such inaccuracies and dependence on temperature variations have been studied, modelled and compensated in order to reach an adequate quality of measurements for the estimation of attitudes. This has been done applying a Kalman Filter-based sensor fusion algorithm that combines sensor models, error parameters and estimation scheme. Attitude estimation from low-cost sensors is first realized in a Matlab/Simulink platform and then implemented on hardware by programming the micro controller and validated. The accuracies of the estimated roll and pitch attitudes are well within the stipulated accuracy level of ±5° for the ALEX. However, the estimation of heading, which is mainly derived from the magnetometer readings, seems to be influenced greatly by the variation in local magnetic field.

  13. Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization

    Directory of Open Access Journals (Sweden)

    Guoliang Chen

    2015-09-01

    Full Text Available Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone’s acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals.

  14. 抑制扩频系统中窄带干扰的新卡尔曼滤波算法%New Kalman Filtering Algorithm for Narrowband Interference Suppression in Spread Spectrum Systems

    Institute of Scientific and Technical Information of China (English)

    许光辉; 胡光锐

    2005-01-01

    A new Kalman filtering algorithm based on estimation of spread spectrum signal before suppression of narrowband interference (NBI) in spread spectrum systems, using the dependence of autoregressive (AR) interference, is presented compared with performance of the ACM nonlinear filtering algorithm, simulation results show that the proposed algorithm has preferable performance, there is about 5 dB SNR improvement in average.

  15. A Fruit Fly-Optimized Kalman Filter Algorithm for Pushing Distance Estimation of a Hydraulic Powered Roof Support through Tuning Covariance

    Directory of Open Access Journals (Sweden)

    Lin Zhang

    2016-10-01

    Full Text Available To measure the pushing distance of a hydraulic-powered roof support, and reduce the cost from a non-reusable displacement sensor embedded in pushing a hydraulic cylinder, an inertial sensor is used to measure the pushing distance, and a Kalman filter is applied to process the inertial data. To obtain better estimation performance, an improved fruit fly optimization algorithm (IFOA is proposed to tune the parameters of the Kalman filter, processing noise covariance Q and observation noise covariance R. The key procedures of the proposed method, including state-space model, fitness function, and Kalman filter implementation, are presented. Finally, an artificial signal is utilized to verify the feasibility of the proposed method, and the tuning results of other algorithms, particle swarm optimization (PSO, genetic algorithm (GA, basic FOA, and 3D-FOA are compared. The proposed method is also applied in the pushing distance estimation scenario. The simulation and application results prove the effectiveness and superiority of the proposed method.

  16. Initializing the WRF Model with Tropical Cyclone Real-Time Reports Using the Ensemble Kalman Filter Algorithm

    Science.gov (United States)

    Du, Tien Duc; Ngo-Duc, Thanh; Kieu, Chanh

    2017-07-01

    This study presents an approach to assimilate tropical cyclone (TC) real-time reports and the University of Wisconsin-Cooperative Institute for Meteorological Satellite Studies (CIMSS) Atmospheric Motion Vectors (AMV) data into the Weather Research and Forecasting (WRF) model for TC forecast applications. Unlike current methods in which TC real-time reports are used to either generate a bogus vortex or spin up a model initial vortex, the proposed approach ingests the TC real-time reports through blending a dynamically consistent synthetic vortex structure with the CIMSS-AMV data. The blended dataset is then assimilated into the WRF initial condition, using the local ensemble transform Kalman filter (LETKF) algorithm. Retrospective experiments for a number of TC cases in the northwestern Pacific basin during 2013-2014 demonstrate that this approach could effectively increase both the TC circulation and enhance the large-scale environment that the TCs are embedded in. Further evaluation of track and intensity forecast errors shows that track forecasts benefit more from improvement in the large-scale flow at 4-5-day lead times, whereas the intensity improvement is minimal. While the difference between the track and intensity improvement could be due to a specific model configuration, this result appears to be consistent with the recent reports of insignificant impacts of inner core data assimilation in operational TC models at the long range of 4-5 days. The new approach will be most beneficial for future regional TC models that are directly initialized from very high-resolution global models whose storm initial locations are sufficiently accurate at the initial analysis that there is no need to carry out any artificial vortex removal or filtering steps.

  17. Kalman Filtering with Inequality Constraints for Turbofan Engine Health Estimation

    Science.gov (United States)

    Simon, Dan; Simon, Donald L.

    2003-01-01

    Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops two analytic methods of incorporating state variable inequality constraints in the Kalman filter. The first method is a general technique of using hard constraints to enforce inequalities on the state variable estimates. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The second method uses soft constraints to estimate state variables that are known to vary slowly with time. (Soft constraints are constraints that are required to be approximately satisfied rather than exactly satisfied.) The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results. The use of the algorithm is demonstrated on a linearized simulation of a turbofan engine to estimate health parameters. The turbofan engine model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.

  18. A Performance Comparison Between Extended Kalman Filter and Unscented Kalman Filter in Power System Dynamic State Estimation

    DEFF Research Database (Denmark)

    Khazraj, Hesam; Silva, Filipe Miguel Faria da; Bak, Claus Leth

    2016-01-01

    Dynamic State Estimation (DSE) is a critical tool for analysis, monitoring and planning of a power system. The concept of DSE involves designing state estimation with Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) methods, which can be used by wide area monitoring to improve...... the stability of power system. State estimation with EKF and UKF methods can be used for monitoring and estimating the dynamic state variables of multi-machine power systems, which are generator rotor speed and rotor angle. This paper uses Powerfactory to solve power flow analysis of simulations, then a non......-linear state estimator is developed in MatLab to solve states by applying the unscented Kalman filter (UKF) and Extended Kalman Filter (EKF) algorithm. Finally, a DSE model is built for a 14 bus power system network to evaluate the proposed algorithm for the networks.This article will focus on comparing...

  19. Kullback-Leibler Divergence Approach to Partitioned Update Kalman Filter

    OpenAIRE

    Raitoharju, Matti; García-Fernández, Ángel F.; Piché, Robert

    2016-01-01

    Kalman filtering is a widely used framework for Bayesian estimation. The partitioned update Kalman filter applies a Kalman filter update in parts so that the most linear parts of measurements are applied first. In this paper, we generalize partitioned update Kalman filter, which requires the use oft the second order extended Kalman filter, so that it can be used with any Kalman filter extension. To do so, we use a Kullback-Leibler divergence approach to measure the nonlinearity of the measure...

  20. Detection of Voltage Disturbance Based on Kalman Filter Algorithm%基于卡尔曼滤波的电压扰动检测算法

    Institute of Scientific and Technical Information of China (English)

    任文琳; 赵庆生; 何志方

    2012-01-01

    In order to real-time detect voltage disturbance in power system, the algorithm based on Kalman filter is presented. It applies a new Kalman model to calculate the effective value of voltage signal, which can achieve real-time tracking of the voltage sag and swell by setting the threshold of voltage RMS deviation with comparative analysis of sliding window RMS algorithm. The simulation results prove the real-time function and reliability of Kalman filter algorithm.%为了对电力系统中的电压扰动进行实时监测,提出了一种基于卡尔曼滤波的电压挠动检测算法,该算法采用新的卡尔曼模型求取电网电压信号的有效值,通过设定电压阈值进而实现电压凹陷和电压凸起波形的实时跟踪,并与滑动窗有效值(RMS)算法进行比较分析,仿真验证了该算法的实时性和可靠性.

  1. Suboptimal distributed Kalman filtering fusion with feedback

    Institute of Scientific and Technical Information of China (English)

    Zhao Minhua; Zhu Zhuanmin; Shi Meng; Peng Qinke; Huang Yongxuan

    2005-01-01

    In order to improve the accuracy of fusion algorithm, feedback is introduced into Kalman filtering fusion. Fusion center broadcasts its latest estimated states to the local sensors, which can improve the performance of local tracking error through reducing the covariance of each local error, and only needs calculating the trace of error variance matrices without calculating the inverse of error variance matrices. Simulation results show that it can reduce the computational complexity and the covariance of error, and it is convenient for engineering applications.

  2. Radio Channel State Prediction by Kalman Filter

    Directory of Open Access Journals (Sweden)

    Peter Ziacik

    2005-01-01

    Full Text Available In this article there is the description Kalman filter using as a radio channel state predictor. Simulator of prediction has been created in MATLAB environment and it is capable to simulate the prediction of radio signal envelope by Clark’s model of radio channel, which is implemented to the simulator. Simulations were realized for prediction range 0.41 ms and 6.24 ms and as comparing criterion we used the prediction error. It is clear from simulations, that with the duration of prediction the prediction error is enlarging, which may cause the erroneous decision of adaptation algorithms.

  3. Kalman filtering approach to blind equalization

    Science.gov (United States)

    Kutlu, Mehmet

    1993-12-01

    Digital communication systems suffer from the channel distortion problem which introduces errors due to intersymbol interference. The solution to this problem is provided by equalizers which use a training sequence to adapt to the channel. However in many cases in which a training sequence is unfeasible, the channel must be adapted blindly. Most of the blind equalization algorithms known so far have problems of convergence to local minima. Our intention is to offer an alternative approach by using extended Kalman filtering and hidden Markov models. They seem to yield more efficient algorithms which take the statistics of the transmitted sequence into consideration. The theoretical development of these new algorithms is discussed in this thesis. Also these algorithms have been simulated under different conditions. The results of simulations and comparisons with existing systems are provided. The models for simulations are presented as MATLAB codes.

  4. Kalman Filter Tracking on Parallel Architectures

    Directory of Open Access Journals (Sweden)

    Cerati Giuseppe

    2016-01-01

    Full Text Available Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. In order to achieve the theoretical performance gains of these processors, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Hadron Collider (HL-LHC, for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques such as Cellular Automata or Hough Transforms. The most common track finding techniques in use today, however, are those based on a Kalman filter approach. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust, and are in use today at the LHC. Given the utility of the Kalman filter in track finding, we have begun to port these algorithms to parallel architectures, namely Intel Xeon and Xeon Phi. We report here on our progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a simplified experimental environment.

  5. Kalman Filter Tracking on Parallel Architectures

    Science.gov (United States)

    Cerati, Giuseppe; Elmer, Peter; Krutelyov, Slava; Lantz, Steven; Lefebvre, Matthieu; McDermott, Kevin; Riley, Daniel; Tadel, Matevž; Wittich, Peter; Würthwein, Frank; Yagil, Avi

    2016-11-01

    Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. In order to achieve the theoretical performance gains of these processors, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Hadron Collider (HL-LHC), for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques such as Cellular Automata or Hough Transforms. The most common track finding techniques in use today, however, are those based on a Kalman filter approach. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust, and are in use today at the LHC. Given the utility of the Kalman filter in track finding, we have begun to port these algorithms to parallel architectures, namely Intel Xeon and Xeon Phi. We report here on our progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a simplified experimental environment.

  6. An Unbiased Unscented Transform Based Kalman Filter for 3D Radar

    Institute of Scientific and Technical Information of China (English)

    WANGGuohong; XIUJianjuan; HEYou

    2004-01-01

    As a derivative-free alternative to the Extended Kalman filter (EKF) in the framework of state estimation, the Unscented Kalman filter (UKF) has potential applications in nonlinear filtering. By noting the fact that the unscented transform is generally biased when converting the radar measurements from spherical coordinates into Cartesian coordinates, a new filtering algorithm for 3D radar, called Unbiased unscented Kalman filter (UUKF), is proposed. The new algorithm is validated by Monte Carlo simulation runs. Simulation results show that the UUKF is more effective than the UKF, EKF and the Converted measurement Kalman filter (CMKF).

  7. Kalman Filter Constraint Tuning for Turbofan Engine Health Estimation

    Science.gov (United States)

    Simon, Dan; Simon, Donald L.

    2005-01-01

    Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints are often neglected because they do not fit easily into the structure of the Kalman filter. Recently published work has shown a new method for incorporating state variable inequality constraints in the Kalman filter, which has been shown to generally improve the filter s estimation accuracy. However, the incorporation of inequality constraints poses some risk to the estimation accuracy as the Kalman filter is theoretically optimal. This paper proposes a way to tune the filter constraints so that the state estimates follow the unconstrained (theoretically optimal) filter when the confidence in the unconstrained filter is high. When confidence in the unconstrained filter is not so high, then we use our heuristic knowledge to constrain the state estimates. The confidence measure is based on the agreement of measurement residuals with their theoretical values. The algorithm is demonstrated on a linearized simulation of a turbofan engine to estimate engine health.

  8. Recursive three-dimensional model reconstruction based on Kalman filtering.

    Science.gov (United States)

    Yu, Ying Kin; Wong, Kin Hong; Chang, Michael Ming Yuen

    2005-06-01

    A recursive two-step method to recover structure and motion from image sequences based on Kalman filtering is described in this paper. The algorithm consists of two major steps. The first step is an extended Kalman filter (EKF) for the estimation of the object's pose. The second step is a set of EKFs, one for each model point, for the refinement of the positions of the model features in the three-dimensional (3-D) space. These two steps alternate from frame to frame. The initial model converges to the final structure as the image sequence is scanned sequentially. The performance of the algorithm is demonstrated with both synthetic data and real-world objects. Analytical and empirical comparisons are made among our approach, the interleaved bundle adjustment method, and the Kalman filtering-based recursive algorithm by Azarbayejani and Pentland. Our approach outperformed the other two algorithms in terms of computation speed without loss in the quality of model reconstruction.

  9. Improved Kalman Filtering Algorithm in Non-Uniformity Correction%改进的卡尔曼滤波非均匀性校正算法

    Institute of Scientific and Technical Information of China (English)

    李晶; 朱斌; 郭立新; 龙波; 王小珂

    2012-01-01

    针对基于卡尔曼滤波(Kalman filtering,KF)的红外焦平面非均匀性校正算法的计算量和存储量较大,不利于实时性校正的缺点,提出一种改进的卡尔曼滤波非均匀性校正算法.该算法通过线性递归滤波器修正了观测方程,用每一帧块图像的统计均值来代替卡尔曼滤波校正算法中的观测矩阵,使增益矩阵得到简化.Matlab仿真实验结果证明:该算法的校正效果与传统的卡尔曼滤波校正算法相当,但大大减少了计算量和存储空间.%Aiming at the big computational complexity and memory requirement of infrared focal plane non-uniformity correction algorithm based on Kalman filtering, it cannot realize real-time correction shortage, an improved Kalman filtering non-uniformity correction algorithm is given. The proposed algorithm modified the observation model by applying linear recursion filter that the observation was instead of the mean value of every block image, then the matrix will be simplified. The simulation of Matlab proves that the computational complexity and memory requirements are significantly reduced, while the correction result is similar.

  10. Spectral diagonal ensemble Kalman filters

    CERN Document Server

    Kasanický, Ivan; Vejmelka, Martin

    2015-01-01

    A new type of ensemble Kalman filter is developed, which is based on replacing the sample covariance in the analysis step by its diagonal in a spectral basis. It is proved that this technique improves the aproximation of the covariance when the covariance itself is diagonal in the spectral basis, as is the case, e.g., for a second-order stationary random field and the Fourier basis. The method is extended by wavelets to the case when the state variables are random fields, which are not spatially homogeneous. Efficient implementations by the fast Fourier transform (FFT) and discrete wavelet transform (DWT) are presented for several types of observations, including high-dimensional data given on a part of the domain, such as radar and satellite images. Computational experiments confirm that the method performs well on the Lorenz 96 problem and the shallow water equations with very small ensembles and over multiple analysis cycles.

  11. Unscented Kalman filter for SINS alignment

    Institute of Scientific and Technical Information of China (English)

    Zhou Zhanxin; Gao Yanan; Chen Jiabin

    2007-01-01

    In order to improve the filter accuracy for the nonlinear error model of strapdown inertial navigation system (SINS) alignment, Unscented Kalman Filter (UKF) is presented for simulation with stationary base and moving base of SINS alignment.Simulation results show the superior performance of this approach when compared with classical suboptimal techniques such as extended Kalman filter in cases of large initial misalignment.The UKF has good performance in case of small initial misalignment.

  12. Improved Kalman Filter-Based Speech Enhancement with Perceptual Post-Filtering

    Institute of Scientific and Technical Information of China (English)

    WEIJianqiang; DULimin; YANZhaoli; ZENGHui

    2004-01-01

    In this paper, a Kalman filter-based speech enhancement algorithm with some improvements of previous work is presented. A new technique based on spectral subtraction is used for separation speech and noise characteristics from noisy speech and for the computation of speech and noise Autoregressive (AR) parameters. In order to obtain a Kalman filter output with high audible quality, a perceptual post-filter is placed at the output of the Kalman filter to smooth the enhanced speech spectra.Extensive experiments indicate that this newly proposed method works well.

  13. Kalman Filter Tracking on Parallel Architectures

    Science.gov (United States)

    Cerati, Giuseppe; Elmer, Peter; Lantz, Steven; McDermott, Kevin; Riley, Dan; Tadel, Matevž; Wittich, Peter; Würthwein, Frank; Yagil, Avi

    2015-12-01

    Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors, but the future will be even more exciting. In order to stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High Luminosity LHC, for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques including Cellular Automata or returning to Hough Transform. The most common track finding techniques in use today are however those based on the Kalman Filter [2]. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust and are exactly those being used today for the design of the tracking system for HL-LHC. Our previous investigations showed that, using optimized data structures, track fitting with Kalman Filter can achieve large speedup both with Intel Xeon and Xeon Phi. We report here our further progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a realistic simulation setup.

  14. Kalman filtering theory and practice with MATLAB

    CERN Document Server

    Grewal, M

    2015-01-01

    The definitive textbook and professional reference on Kalman Filtering fully updated, revised, and expanded This book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control. Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.

  15. RSSI based indoor tracking in sensor networks using Kalman filters

    DEFF Research Database (Denmark)

    Tøgersen, Frede Aakmann; Skjøth, Flemming; Munksgaard, Lene;

    2010-01-01

    We propose an algorithm for estimating positions of devices in a sensor network using Kalman filtering techniques. The specific area of application is monitoring the movements of cows in a barn. The algorithm consists of two filters. The first filter enhances the signal-to-noise ratio...... of the observed signal strengths and gives interpolated values at specific timestamps. Information from the first filter is transferred to the second filter which estimates the positions. Methods for estimating the parameters of the filters are given and these provide a straightforward calibration of the system...

  16. Unscented Kalman filters for polarization state tracking and phase noise mitigation.

    Science.gov (United States)

    Jignesh, Jokhakar; Corcoran, Bill; Zhu, Chen; Lowery, Arthur

    2016-09-19

    Simultaneous polarization and phase noise tracking and compensation is proposed based on an unscented Kalman filter (UKF). We experimentally demonstrate the tracking under noise-loading and after 800-km single-mode fiber transmission with 20-Gbaud QPSK and 16-QAM signals. These experiments show that the proposed UKF outperforms both conventional blind tracing algorithms and a previously proposed extended Kalman filter, at the cost of higher complexity. Additionally, we propose and test modified Kalman filter algorithms to reduce computational complexity.

  17. Dual extended Kalman filtering in recurrent neural networks(1).

    Science.gov (United States)

    Leung, Chi-Sing; Chan, Lai-Wan

    2003-03-01

    In the classical deterministic Elman model, the estimation of parameters must be very accurate. Otherwise, the system performance is very poor. To improve the system performance, we can use a Kalman filtering algorithm to guide the operation of a trained recurrent neural network (RNN). In this case, during training, we need to estimate the state of hidden layer, as well as the weights of the RNN. This paper discusses how to use the dual extended Kalman filtering (DEKF) for this dual estimation and how to use our proposing DEKF for removing some unimportant weights from a trained RNN. In our approach, one Kalman algorithm is used for estimating the state of the hidden layer, and one recursive least square (RLS) algorithm is used for estimating the weights. After training, we use the error covariance matrix of the RLS algorithm to remove unimportant weights. Simulation showed that our approach is an effective joint-learning-pruning method for RNNs under the online operation.

  18. Finite Element Modelling of a Field-Sensed Magnetic Suspended System for Accurate Proximity Measurement Based on a Sensor Fusion Algorithm with Unscented Kalman Filter

    Directory of Open Access Journals (Sweden)

    Amor Chowdhury

    2016-09-01

    Full Text Available The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation.

  19. Finite Element Modelling of a Field-Sensed Magnetic Suspended System for Accurate Proximity Measurement Based on a Sensor Fusion Algorithm with Unscented Kalman Filter.

    Science.gov (United States)

    Chowdhury, Amor; Sarjaš, Andrej

    2016-09-15

    The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation.

  20. Kalman Filter Based Tracking in an Video Surveillance System

    Directory of Open Access Journals (Sweden)

    SULIMAN, C.

    2010-05-01

    Full Text Available In this paper we have developed a Matlab/Simulink based model for monitoring a contact in a video surveillance sequence. For the segmentation process and corect identification of a contact in a surveillance video, we have used the Horn-Schunk optical flow algorithm. The position and the behavior of the correctly detected contact were monitored with the help of the traditional Kalman filter. After that we have compared the results obtained from the optical flow method with the ones obtained from the Kalman filter, and we show the correct functionality of the Kalman filter based tracking. The tests were performed using video data taken with the help of a fix camera. The tested algorithm has shown promising results.

  1. A modified iterative ensemble Kalman filter data assimilation method

    Science.gov (United States)

    Xu, Baoxiong; Bai, Yulong; Wang, Yizhao; Li, Zhe; Ma, Boyang

    2017-08-01

    High nonlinearity is a typical characteristic associated with data assimilation systems. Additionally, iterative ensemble based methods have attracted a large amount of research attention, which has been focused on dealing with nonlinearity problems. To solve the local convergence problem of the iterative ensemble Kalman filter, a modified iterative ensemble Kalman filter algorithm was put forward, which was based on a global convergence strategy from the perspective of a Gauss-Newton iteration. Through self-adaption, the step factor was adjusted to enable every iteration to approach expected values during the process of the data assimilation. A sensitivity experiment was carried out in a low dimensional Lorenz-63 chaotic system, as well as a Lorenz-96 model. The new method was tested via ensemble size, observation variance, and inflation factor changes, along with other aspects. Meanwhile, comparative research was conducted with both a traditional ensemble Kalman filter and an iterative ensemble Kalman filter. The results showed that the modified iterative ensemble Kalman filter algorithm was a data assimilation method that was able to effectively estimate a strongly nonlinear system state.

  2. Hierarchical Bayes Ensemble Kalman Filtering

    CERN Document Server

    Tsyrulnikov, Michael

    2015-01-01

    Ensemble Kalman filtering (EnKF), when applied to high-dimensional systems, suffers from an inevitably small affordable ensemble size, which results in poor estimates of the background error covariance matrix ${\\bf B}$. The common remedy is a kind of regularization, usually an ad-hoc spatial covariance localization (tapering) combined with artificial covariance inflation. Instead of using an ad-hoc regularization, we adopt the idea by Myrseth and Omre (2010) and explicitly admit that the ${\\bf B}$ matrix is unknown and random and estimate it along with the state (${\\bf x}$) in an optimal hierarchical Bayes analysis scheme. We separate forecast errors into predictability errors (i.e. forecast errors due to uncertainties in the initial data) and model errors (forecast errors due to imperfections in the forecast model) and include the two respective components ${\\bf P}$ and ${\\bf Q}$ of the ${\\bf B}$ matrix into the extended control vector $({\\bf x},{\\bf P},{\\bf Q})$. Similarly, we break the traditional backgrou...

  3. 自适应Kalman滤波算法在加速度计自标定中的应用%Application of adaptive Kalman filtering algorithm in autonomous calibration accelerometer

    Institute of Scientific and Technical Information of China (English)

    叶军; 陈坚; 石国祥

    2011-01-01

    针对自标定加速度计组合动基座试验数据中存在的数据异常问题,推导并运用自适应Kalman滤波算法剔除异常数据,通过对不同Kalman滤波算法自标定精度解算结果的均值和标准差进行比较,表明自适应Kalman滤波算法更加有效.%Aiming at the problems of abnormal data in the test data of autonomous calibration accelerometer-unit on dynamicbase,deducing and using adaptive Kalman filtering algorithm eliminates abnormal data, according the comparison of results from calibration precision by different Kalman filtering algorithm, it shows that the adaptive Kalman filtering algorithm is more effective.

  4. Harmonic Detection at Initialization With Kalman Filter

    DEFF Research Database (Denmark)

    Hussain, Dil Muhammad Akbar; Imran, Raja Muhammad; Shoro, Ghulam Mustafa

    2014-01-01

    the affect of harmonics on the supply. For the detection of these harmonics various techniques are available and one of that technique is the Kalman filter. In this paper we investigate that what are the consequences when harmonic detection system based on Kalman Filtering is initialized......Most power electronic equipment these days generate harmonic disturbances, these devices hold nonlinear voltage/current characteristic. The harmonics generated can potentially be harmful to the consumer supply. Typically, filters are integrated at the power source or utility location to filter out...

  5. FUZZY OPTIMIZATION USING EXTENDED KALMAN FILTER

    Directory of Open Access Journals (Sweden)

    M.DIVYA

    2013-01-01

    Full Text Available Fuzzy Logic is based on the idea that in fuzzy sets each element in the set can assume a value from 0 to 1, not only 0 or 1, as in crisp set theory. The degree of membership function is defined as the gradation in the extent to which an element is belonging to the relevant sets. Optimizing the membership functions of a fuzzy system can be viewed as a system identification problem for nonlinear dynamic system. In this paper two input and one output fuzzy controller is designed for the dynamic process of aircraft. The addition of an EKF in the feedback loop improved the system response by blocking possible effects of measurement error based on Predictor-Corrector algorithm. An Extended Kalman Filter approach to optimize the membership functions of the inputs and outputs of the fuzzy controller. The performance of the fuzzy system before and after the optimization are compared, as well as the membership functions.

  6. Kalman Filter Tracking on Parallel Architectures

    CERN Document Server

    Cerati, Giuseppe; Lantz, Steven; McDermott, Kevin; Riley, Dan; Tadel, Matevž; Wittich, Peter; Würthwein, Frank; Yagil, Avi

    2015-01-01

    Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors, but the future will be even more exciting. In order to stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High Luminosity LHC, for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques including Cellular Automata or returning to Hough Transform. The most common track finding techniques in use today are however those based on the Kalman Filter. Significant experience has...

  7. Kalman Filter Tracking on Parallel Architectures

    CERN Document Server

    Cerati, Giuseppe; Krutelyov, Slava; Lantz, Steven; Lefebvre, Matthieu; McDermott, Kevin; Riley, Daniel; Tadel, Matevz; Wittich, Peter; Wuerthwein, Frank; Yagil, Avi

    2016-01-01

    Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. To stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Hadron Collider (HL-LHC), for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques such as Cellular Automata or Hough Transforms. The most common track finding techniques in use today, however, are those based on the Kalman Filter. Significant experience has been accumulated with these techniques on real tracking detector sy...

  8. Vehicle Tracking Using Kalman Filter and Features

    Directory of Open Access Journals (Sweden)

    Amir Salarpour

    2011-09-01

    Full Text Available Vehicle tracking has a wide variety of applications. The image resolution of the video available from most traffic camera system is low. In many cases for tracking multi object, distinguishing them from another isn’t easy because of their similarity. In this paper we describe a method, for tracking multiple objects, where the objects are vehicles. The number of vehicles is unknown and varies. We detect all moving objects, and for tracking of vehicle we use the kalman filter and color feature and distance of it from one frame to the next. So the method can distinguish and tracking all vehicles individually. The proposed algorithm can be applied to multiple moving objects.

  9. Subspace System Identification of the Kalman Filter

    Directory of Open Access Journals (Sweden)

    David Di Ruscio

    2003-07-01

    Full Text Available Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati equation. Furthermore, it is in general and for colored inputs, proved that the subspace identification of the states only is possible if the deterministic part of the system is known or identified beforehand. However, if the inputs are white, then, it is proved that the states can be identified directly. Some alternative projection matrices which can be used to compute the extended observability matrix directly from the data are presented. Furthermore, an efficient method for computing the deterministic part of the system is presented. The closed loop subspace identification problem is also addressed and it is shown that this problem is solved and unbiased estimates are obtained by simply including a filter in the feedback. Furthermore, an algorithm for consistent closed loop subspace estimation is presented. This algorithm is using the controller parameters in order to overcome the bias problem.

  10. Iterated unscented Kalman filter for phase unwrapping of interferometric fringes.

    Science.gov (United States)

    Xie, Xianming

    2016-08-22

    A fresh phase unwrapping algorithm based on iterated unscented Kalman filter is proposed to estimate unambiguous unwrapped phase of interferometric fringes. This method is the result of combining an iterated unscented Kalman filter with a robust phase gradient estimator based on amended matrix pencil model, and an efficient quality-guided strategy based on heap sort. The iterated unscented Kalman filter that is one of the most robust methods under the Bayesian theorem frame in non-linear signal processing so far, is applied to perform simultaneously noise suppression and phase unwrapping of interferometric fringes for the first time, which can simplify the complexity and the difficulty of pre-filtering procedure followed by phase unwrapping procedure, and even can remove the pre-filtering procedure. The robust phase gradient estimator is used to efficiently and accurately obtain phase gradient information from interferometric fringes, which is needed for the iterated unscented Kalman filtering phase unwrapping model. The efficient quality-guided strategy is able to ensure that the proposed method fast unwraps wrapped pixels along the path from the high-quality area to the low-quality area of wrapped phase images, which can greatly improve the efficiency of phase unwrapping. Results obtained from synthetic data and real data show that the proposed method can obtain better solutions with an acceptable time consumption, with respect to some of the most used algorithms.

  11. Study of the Effectiveness of Different Kalman Filtering Methods and Smoothers in Object Tracking Based on Simulation Tests

    Directory of Open Access Journals (Sweden)

    Malinowski Marcin

    2015-02-01

    Full Text Available In navigation practice, there are various navigational architecture and integration strategies of measuring instruments that affect the choice of the Kalman filtering algorithm. The analysis of different methods of Kalman filtration and associated smoothers applied in object tracing was made on the grounds of simulation tests of algorithms designed and presented in this paper. EKF (Extended Kalman Filter filter based on approximation with (jacobians partial derivations and derivative-free filters like UKF (Unscented Kalman Filter and CDKF (Central Difference Kalman Filter were implemented in comparison. For each method of filtration, appropriate smoothers EKS (Extended Kalman Smoother, UKS (Unscented Kalman Smoother and CDKS (Central Difference Kalman Smoother were presented as well. Algorithms performance is discussed on the theoretical base and simulation results of two cases are presented.

  12. Unscented Kalman Filter for Autonomous Warship Attitude Determination

    Institute of Scientific and Technical Information of China (English)

    FU Jian-guo; WANG Xiao-tong; JIN Lian-gan; MA Ye

    2005-01-01

    To address a problem of autonomous attitude determination algorithm using gravitational field and geomagnetic field observation, a new recursive optimization autonomous attitude estimation algorithm is proposed. The algorithm is based on unscented Kalman filter(UKF), and can synchronously provide the attitude rate information. The simulated results show that the measurement precision of the method could be increased by 2 times compared to that of the common methods.

  13. A numerical storm surge forecast model with Kalman filter

    Institute of Scientific and Technical Information of China (English)

    Yu Fujiang; Zhang Zhanhai; Lin Yihua

    2001-01-01

    Kalman filter data assimilation technique is incorporated into a standard two-dimensional linear storm surge model. Imperfect model equation and imperfect meteorological forcimg are accounted for by adding noise terms to the momentum equations. The deterministic model output is corrected by using the available tidal gauge station data. The stationary Kalman filter algorithm for the model domain is calculated by an iterative procedure using specified information on the inaccuracies in the momentum equations and specified error information for the observations. An application to a real storm surge that occurred in the summer of 1956 in the East China Sea is performed by means of this data assimilation technique. The result shows that Kalman filter is useful for storm surge forecast and hindcast.

  14. Characterization and Implementation of a Real-World Target Tracking Algorithm on Field Programmable Gate Arrays with Kalman Filter Test Case

    Science.gov (United States)

    2008-03-01

    21]. Table 2.1: Discrete Kalman filter time update equations x−k = Axk−1 + Buk (2.8) P−k = APk −1A T + Q (2.9) a posteriori error covariance [21...K*residual(:,k); Table 2.3: Comparison: Matlabr code and Kalman filter equations x−k = Axk−1 + Buk ⇐⇒ x(:, k) = phi ∗ x(:, k − 1) P−k = APk −1A T

  15. Scheme of adaptive polarization filtering based on Kalman model

    Institute of Scientific and Technical Information of China (English)

    Song Lizhong; Qi Haiming; Qiao Xiaolin; Meng Xiande

    2006-01-01

    A new kind of adaptive polarization filtering algorithm in order to suppress the angle cheating interference for the active guidance radar is presented. The polarization characteristic of the interference is dynamically tracked by using Kalman estimator under variable environments with time. The polarization filter parameters are designed according to the polarization characteristic of the interference, and the polarization filtering is finished in the target cell. The system scheme of adaptive polarization filter is studied and the tracking performance of polarization filter and improvement of angle measurement precision are simulated. The research results demonstrate this technology can effectively suppress the angle cheating interference in guidance radar and is feasible in engineering.

  16. 基于自适应渐消 EKF 的 FastSLAM 算法%FastSLAM algorithm based on adaptive fading extended Kalman filter

    Institute of Scientific and Technical Information of China (English)

    刘丹; 段建民; 于宏啸

    2016-01-01

    快速同时定位与建图(fast simultaneous localization and mapping,FastSLAM)算法的采样过程会带来粒子退化问题,为了改进算法的性能,提高估计精度,从研究粒子滤波的建议分布函数出发,提出基于自适应渐消扩展卡尔曼滤波(adaptive fading extended Kalman filter,AFEKF)的 FastSLAM 算法。该算法基于 FastSLAM的基本框架,利用 AFEKF 产生一种参数可自适应调节的建议分布函数,使其更接近移动机器人的后验位姿概率分布,减缓粒子集的退化。因此在同等粒子数的情况下,该算法有效提高了 SLAM 精度,以此减少所使用的粒子数,降低算法的复杂度。基于模拟器和标准数据集的实验仿真结果验证了该算法的有效性。%Sampling process often causes particle degradation in fast simultaneous localization and mapping (FastSLAM).From the point view of the proposal distribution function,a method named the FastSLAM based on adaptive fading extended Kalman filter is proposed to improve the performance of the algorithm and increase estimation accuracy.It uses the adaptive fading extended Kalman filter (AFEKF)to compute proposal distribu-tion based on the basic framework of FastSLAM,then this proposal distribution is more close to the posterior position of the mobile robot and the degree of particle degradation is reduced.In the case of the same number of particles,the algorithm can effectively improve the accuracy of SLAM.Hence it can reduce the number of parti-cles used in the algorithm and the complexity of the algorithm.The validity of the proposed algorithm is verified by the experimental simulation results based on the simulator and the standard data set.

  17. A New Filtering Algorithm Utilizing Radial Velocity Measurement

    Institute of Scientific and Technical Information of China (English)

    LIU Yan-feng; DU Zi-cheng; PAN Quan

    2005-01-01

    Pulse Doppler radar measurements consist of range, azimuth, elevation and radial velocity. Most of the radar tracking algorithms in engineering only utilize position measurement. The extended Kalman filter with radial velocity measureneut is presented, then a new filtering algorithm utilizing radial velocity measurement is proposed to improve tracking results and the theoretical analysis is also given. Simulation results of the new algorithm, converted measurement Kalman filter, extended Kalman filter are compared. The effectiveness of the new algorithm is verified by simulation results.

  18. Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation.

    Science.gov (United States)

    Liu, Xi; Qu, Hua; Zhao, Jihong; Yue, Pengcheng; Wang, Meng

    2016-09-20

    A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC), the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT) is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm.

  19. New Adaptive Active Queue Management Algorithm with Kalman Filter%自适应卡尔曼滤波的主动队列管理算法

    Institute of Scientific and Technical Information of China (English)

    闫巧; 胡晓娟; 雷琼钰

    2012-01-01

    controller accelerates the regulation speed of the controller through differential factor. But the parameters of PID controller are fixed,they can't be adapted with dynamic network,so the stability of the queue can't be controlled effectively. A new adaptive active queue management(AQM) algorithm with Kalman filter was presented according to the adaptivity of the neural network The new algorithm combines Kalman filter law with neural network, which has the merits of both. It can determinate future queue length based on queue lengths and some rates of change in the queue length. The results of simulation show that the new AQM algorithm is superior to the typical PID controller on the queue stability, time delay and link utilization.%PID控制器通过微分环节加快了控制器的调节速度,但PID的参数是固定的,不能根据动态的网络自调整参数,故不能有效控制队列的稳定性.由于神经元网络有自适应性,提出了一种自适应卡尔曼滤波的主动队列管理算法(adaptive-KF-AQM).它结合卡尔曼滤波和神经元网络方法,根据队列长度及其变化率来估计下一时刻的队列长度,使队列长度在期望值附近波动.仿真结果表明,该算法在队列稳定性、收敛速度、延时和链路利用率等方面都明显优于传统的PID算法.

  20. 基于卡尔曼滤波的运动人体跟踪算法研究%Research on Moving Human Tracking Algorithm Based on Kalman Filter

    Institute of Scientific and Technical Information of China (English)

    乔坤; 郭朝勇; 史进伟

    2012-01-01

    提出一种基于卡尔曼滤波的运动目标快速跟踪算法.利用卡尔曼滤波器的预测功能,预测运动人体目标在下一帧中的位置,在Matlab仿真环境下实现该跟踪算法,实验结果表明:该算法对人体目标的运动趋势能够做出正确的预测估计,跟踪效果和性能较为稳定和可靠.此外,该算法将图像全局搜索问题转换为局部搜索,使运算量减少,满足实时性跟踪要求,实现了对运动目标的快速跟踪.%A real-time moving object tracking algorithm based on Kalman filter is proposed The possible position of the moving human in the next frame is predicted by Kalman filter's predictive function. Based on the Matlab simulation environment to achieve the tracking algorithm and the experimental results show that the algorithm can correctly estimate the human's motion trend and the tracking results and performance is better. In addition, the global searching scope of an image is converted to local scope, thus reduce the computation and meet the requirements of real-time tracking, and the speedy tracking of moving human is realized.

  1. Design of Kalman filters for mobile robots

    DEFF Research Database (Denmark)

    Larsen, Thomas Dall; Hansen, Karsten L.; Andersen, Nils Axel

    1999-01-01

    Kalman filters have for a long time been widely used on mobile robots as a location estimator. Many different Kalman filter designs have been proposed, using models of various complexity. In this paper, two different design methods are evaluated and compared. Focus is put on the common setup where...... the mobile robot is equipped with a dual encoder system supported by some additional absolute measurements. A common filter type for this setup is the odometric filter, where readings from the odometry system on the robot are used together with the geometry of the robot movement as a model of the robot....... If additional kinematic assumptions are made, for instance regarding the velocity of the robot, an augmented model can be used instead. This kinematic filter has some advantages when used intelligently, and it is shown how this type of filter can be used to suppress noise on encoder readings and velocity...

  2. Adaptive robust Kalman filtering for precise point positioning

    Science.gov (United States)

    Guo, Fei; Zhang, Xiaohong

    2014-10-01

    The optimality of precise point postioning (PPP) solution using a Kalman filter is closely connected to the quality of the a priori information about the process noise and the updated mesurement noise, which are sometimes difficult to obtain. Also, the estimation enviroment in the case of dynamic or kinematic applications is not always fixed but is subject to change. To overcome these problems, an adaptive robust Kalman filtering algorithm, the main feature of which introduces an equivalent covariance matrix to resist the unexpected outliers and an adaptive factor to balance the contribution of observational information and predicted information from the system dynamic model, is applied for PPP processing. The basic models of PPP including the observation model, dynamic model and stochastic model are provided first. Then an adaptive robust Kalmam filter is developed for PPP. Compared with the conventional robust estimator, only the observation with largest standardized residual will be operated by the IGG III function in each iteration to avoid reducing the contribution of the normal observations or even filter divergence. Finally, tests carried out in both static and kinematic modes have confirmed that the adaptive robust Kalman filter outperforms the classic Kalman filter by turning either the equivalent variance matrix or the adaptive factor or both of them. This becomes evident when analyzing the positioning errors in flight tests at the turns due to the target maneuvering and unknown process/measurement noises.

  3. Restricted Kalman Filtering Theory, Methods, and Application

    CERN Document Server

    Pizzinga, Adrian

    2012-01-01

    In statistics, the Kalman filter is a mathematical method whose purpose is to use a series of measurements observed over time, containing random variations and other inaccuracies, and produce estimates that tend to be closer to the true unknown values than those that would be based on a single measurement alone. This Brief offers developments on Kalman filtering subject to general linear constraints. There are essentially three types of contributions: new proofs for results already established; new results within the subject; and applications in investment analysis and macroeconomics, where th

  4. Kalman filter estimation model in flood forecasting

    Science.gov (United States)

    Husain, Tahir

    Elementary precipitation and runoff estimation problems associated with hydrologic data collection networks are formulated in conjunction with the Kalman Filter Estimation Model. Examples involve the estimation of runoff using data from a single precipitation station and also from a number of precipitation stations. The formulations demonstrate the role of state-space, measurement, and estimation equations of the Kalman Filter Model in flood forecasting. To facilitate the formulation, the unit hydrograph concept and antecedent precipitation index is adopted in the estimation model. The methodology is then applied to estimate various flood events in the Carnation Creek of British Columbia.

  5. Nonlinear Kalman Filtering in Affine Term Structure Models

    DEFF Research Database (Denmark)

    Christoffersen, Peter; Dorion, Christian; Jacobs, Kris;

    2014-01-01

    The extended Kalman filter, which linearizes the relationship between security prices and state variables, is widely used in fixed-income applications. We investigate whether the unscented Kalman filter should be used to capture nonlinearities and compare the performance of the Kalman filter...... with that of the particle filter. We analyze the cross section of swap rates, which are mildly nonlinear in the states, and cap prices, which are highly nonlinear. When caps are used to filter the states, the unscented Kalman filter significantly outperforms its extended counterpart. The unscented Kalman filter also...

  6. A data fusion Kalman filter algorithm to estimate leaf area index evolution by using Modis LAI and PROBA-V top of canopy synthesis data

    Science.gov (United States)

    Novelli, Antonio

    2016-08-01

    Leaf Area Index (LAI) is essential in ecosystem and agronomic studies, since it measures energy and gas exchanges between vegetation and atmosphere. In the last decades, LAI values have widely been estimated from passive remotely sensed data. Common approaches are based on semi-empirical/statistic techniques or on radiative transfer model inversion. Although the scientific community has been providing several LAI retrieval methods, the estimated results are often affected by noise and measurement uncertainties. The sequential data assimilation theory provides a theoretical framework to combine an imperfect model with incomplete observation data. In this document a data fusion Kalman filter algorithm is proposed in order to estimate the time evolution of LAI by combining MODIS LAI data and PROBA-V surface reflectance data. The reflectance data were linked to LAI by using the Reduced Simple Ratio index. The main working hypotheses were lacking input data necessary for climatic models and canopy reflectance models.

  7. Performance enhancement for GPS positioning using constrained Kalman filtering

    Science.gov (United States)

    Guo, Fei; Zhang, Xiaohong; Wang, Fuhong

    2015-08-01

    Over the past decades Kalman filtering (KF) algorithms have been extensively investigated and applied in the area of kinematic positioning. In the application of KF in kinematic precise point positioning (PPP), it is often the case where some known functional or theoretical relations exist among the unknown state parameters, which can be and should be made use of to enhance the performance of kinematic PPP, especially in an urban and forest environment. The central task of this paper is to effectively blend the commonly used GNSS data and internal/external additional constrained information to generate an optimal PPP solution. This paper first investigates the basic algorithm of constrained Kalman filtering. Then two types of PPP model with speed constraints and trajectory constraints, respectively, are proposed. Further validation tests based on a variety of situations show that the positioning performances (positioning accuracy, reliability and continuity) from the constrained Kalman filter are significantly superior to those from the conventional Kalman filter, particularly under extremely poor observation conditions.

  8. Maneuvering Target Tracking Algorithm Based on Adaptive Fading Kalman Filter%基于改进 Singer 模型的机动目标跟踪方法

    Institute of Scientific and Technical Information of China (English)

    张燕; 柳超; 李云鹏

    2015-01-01

    Considering the divergence and poor precision due to inaccurate modeling of the system or noise, an a-daptive fading kalman filter based on singer model (AKF - Singer)is proposed. It adopts a fading factor to restrain the memory length of kalman filter. Simulation shows that the proposed algorithm can effectively restrain the diver-gence of filtering, and is more steady and more precise compared to traditional singer model.%Singer 模型使用标准卡尔曼滤波器对机动目标进行跟踪,当系统模型不准确或噪声统计特性不确定时,容易引起滤波发散或跟踪精度下降等问题。针对这种情况,本文提出了一种采用自适应渐消卡尔曼滤波的 Singer 模型算法(AKF Singer),通过引入渐消因子来抑制滤波器的记忆长度,自适应的调整新息权重和滤波器增益,从而避免发散。仿真结果表明,本文所提算法能够有效抑制滤波发散,相比于传统 Singer 模型,具有更好的跟踪稳定性和更高的跟踪精度。

  9. Kalman Filter Application to Symmetrical Fault Detection during Power Swing

    DEFF Research Database (Denmark)

    Khodaparast, Jalal; Silva, Filipe Miguel Faria da; Khederzadeh, M.;

    2016-01-01

    capability of Kalman Filter. The proposed index is calculated by assessing the difference between predicted and actual samples of impedance. The predicted impedance samples are obtained using Kalman filter and Taylor expansion, which is used in this paper to track the phasor precisely. Second order of Taylor...... expansion is used to decrease corrugation effect of impedance estimation and increase the reliability of proposed method. The instantaneous estimation and prediction capability of Kalman filter are two reasons for proposing utilizing Kalman filter....

  10. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems

    Directory of Open Access Journals (Sweden)

    Chien-Hao Tseng

    2016-07-01

    Full Text Available This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF and fuzzy logic adaptive system (FLAS for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF, unscented Kalman filter (UKF, and CKF approaches.

  11. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.

    Science.gov (United States)

    Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing

    2016-07-26

    This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.

  12. 光电跟踪系统非线性新息自适应卡尔曼滤波算法%Nonlinear Innovation Adaptive Kalman Filter Algorithm for Electro-optical Tracking

    Institute of Scientific and Technical Information of China (English)

    王秋平; 左玲; 康顺

    2011-01-01

    为解决非线性部分状态卡尔曼滤波算法中由于线性化误差所导致的滤波精度下降问题,提出采用UT变换方法计算系统状态误差方差,及基于新息自适应调整系统噪声方差,进而构成一种新的非线性自适应部分状态卡尔曼滤波算法,并总结出详细算法结构.同时,将此方法应用到非线性测量光电跟踪系统中,并与U卡尔曼滤波和非线性部分状态卡尔曼滤波进行性能对比.仿真实验结果证明,将UT变换和基于新息自适应调整系统噪声方差方法引入部分状态卡尔曼滤波是有效的,而且其性能明显优于U卡尔曼滤波和非线性部分状态卡尔曼滤波.%In order to solve the problem of accuracy decline caused by the linearization error in nonlinear reduced state Kalman filter, a new nonlinear adaptive reduced state Kalman filter algorithm is provided by using UT transformation to calculate the covariance of the system state error and modify adaptively the system noise covariance based on innovation,and the algorithm structure is summarized in detail. Then, the algorithm is applied in nonlinear measurement electro-optical tracking system and the performances of nonlinear adaptive reduced state Kalman filter were compared with unscented Kalman filter and nonlinear reduced state Kalman filter. The Matlab simulation results show that applying UT transformation and modifying adaptively the system noise covariance based on innovation in reduced state Kalman filter is valid, and the performance outperforms those of the unscented Kalman filter and nonlinear reduced state Kalman filter.

  13. Reduced-order Kalman filtering with incomplete observability

    Science.gov (United States)

    Yonezawa, K.

    1980-01-01

    Kalman filtering is considered with reference to linear stochastic dynamic systems without complete observability. It is shown that the canonical decomposition theorem can be extended to the stochastic case and the matrix Riccati equation of the Kalman filter is order-reducible if some states are not observable. The inclusion of unobservable states in Kalman filtering makes the unobservable states 'asymptotically' observable in the filter if these unobservable states are dynamically connected to observable states and asymptotically stable. The reduced-order Kalman filter saves computation time when compared to the conventional Kalman filter.

  14. Multiple Fading Factors Kalman Filter for SINS Static Alignment Application

    Institute of Scientific and Technical Information of China (English)

    GAO Weixi; MIAO Lingjuan; NI Maolin

    2011-01-01

    To solve the problem that the standard Kalman filter cannot give the optimal solution when the system model and stochastic information are unknown accurately,single fading factor Kalman filter is suitable for simple systems.But for complex systems with multi-variable,it may not be sufficient to use single fading factor as a multiplier for the covariance matrices.In this paper,a new multiple fading factors Kalman filtering algorithm is presented.By calculating the unbiased estimate of the innovation sequence covariance using fenestration,the fading factor matrix is obtained.Adjusting the covariance matrix of prediction error Pk|k-1 using fading factor matrix,the algorithm provides different rates of fading for different filter channels.The proposed algorithm is applied to strapdown inertial navigation system(SINS) initial alignment,and simulation and experimental results demonstrate that,the alignment accuracy can be upgraded dramatically when the actual system noise characteristics are different from the pre-set values.The new algorithm is less sensitive to uncertainty noise and has better estimation effect of the parameters.Therefore,it is of significant value in practical applications.

  15. A Tool for Kalman Filter Tuning

    DEFF Research Database (Denmark)

    Åkesson, Bernt Magnus; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad

    2007-01-01

    The Kalman filter requires knowledge about the noise statistics. In practical applications, however, the noise covariances are generally not known. A method for estimating noise covariances from process data has been investigated. The method gives a least-squares estimate of the noise covariances...

  16. Towards self-organizing Kalman filters

    NARCIS (Netherlands)

    Sijs, J.; Papp, Z.

    2012-01-01

    Distributed Kalman filtering is an important signal processing method for state estimation in large-scale sensor networks. However, existing solutions do not account for unforeseen events that are likely to occur and thus dramatically changing the operational conditions (e.g. node failure, communica

  17. Industrial applications of the Kalman filter

    DEFF Research Database (Denmark)

    Auger, François; Hilairet, Mickael; Guerrero, Josep M.

    2013-01-01

    The Kalman filter has received a huge interest from the industrial electronics community and has played a key role in many engineering fields since the 70s, ranging, without being exhaustive, trajectory estimation, state and parameter estimation for control or diagnosis, data merging, signal...

  18. Selection of noise parameters for Kalman filter

    Institute of Scientific and Technical Information of China (English)

    Ka-Veng Yuen; Ka-In Hoi; Kai-Meng Mok

    2007-01-01

    The Bayesian probabilistic approach is proposed to estimate the process noise and measurement noise parameters for a Kalman filter. With state vectors and covariance matrices estimated by the Kalman filter, the likehood of the measurements can be constructed as a function of the process noise and measurement noise parameters. By maximizing the likklihood function with respect to these noise parameters, the optimal values can be obtained. Furthermore, the Bayesian probabilistic approach allows the associated uncertainty to be quantified. Examples using a single-degree-of-freedom system and a ten-story building illustrate the proposed method. The effect on the performance of the Kalman filter due to the selection of the process noise and measurement noise parameters was demonstrated. The optimal values of the noise parameters were found to be close to the actual values in the sense that the actual parameters were in the region with significant probability density. Through these examples, the Bayesian approach was shown to have the capability to provide accurate estimates of the noise parameters of the Kalman filter, and hence for state estimation.

  19. Steady-State Performance of Kalman Filter for DPLL

    Institute of Scientific and Technical Information of China (English)

    QIAN Yi; CUI Xiaowei; LU Mingquan; FENG Zhenming

    2009-01-01

    For certain system models, the structure of the Kalman filter is equivalent to a second-order vari-able gain digital phase-locked loop (DPLL). To apply the knowledge of DPLLs to the design of Kalman filters, this paper studies the steady-state performance of Kalman filters for these system models. The results show that the steady-state Kalman gain has the same form as the DPLL gain. An approximate simple form for the steady-state Kalman gain is used to derive an expression for the equivalent loop bandwidth of the Kalman filter as a function of the process and observation noise variances. These results can be used to analyze the steady-state performance of a Kalman filter with DPLL theory or to design a Kalman filter model with the same steady-state performance as a given DPLL.

  20. A Kalman filter algorithm for single-frequency RTK solution%单频RTK动态解算的卡尔曼滤波算法研究

    Institute of Scientific and Technical Information of China (English)

    徐彦田; 程鹏飞; 蔡艳辉; 徐宗秋

    2012-01-01

    本文针对单频RTK提出了一种快速动态定位卡尔曼滤波算法,该方法使用C码和L1观测值,用模型改正对流层干延迟,双差大气延迟分解为测站大气天顶延迟和投影函数,与流动站位置以及站间单差模糊度组成观测方程进行卡尔曼滤波,得到单差模糊度浮点解及方差阵,通过星间求差得到双差模糊度浮点解及方差阵,结合MLAMBDA方法实时动态确定模糊度.经实测数据和IGS站数据验证该算法具有较好定位结果.%In this paper, a Kalman filter algorithm suitable for single-frequency RTK solution was proposed using C code and LI phase measurements. Firstly, the tropospheric dry delay was predicted with model, double-difference atmospheric delay was estimated in a manner of the atmospheric zenith delay, then established observation equation for Kalman filter with the rover position, and single-difference ambiguity between stations; secondly, the double-difference ambiguity and covariance matrix were solved by making difference between satellites, after obtaining single-difference ambiguity and covariance matrix; In the end, a Modified Least-square AMBi-guity Decorelation Adjustment (MLAMBDA) method was used to directly fix double-difference ambiguities. Some numerical tests were shown which indicated that the proposed method could get better positioning.

  1. Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

    Directory of Open Access Journals (Sweden)

    Xiangwei Guo

    2016-02-01

    Full Text Available An estimation of the power battery state of charge (SOC is related to the energy management, the battery cycle life and the use cost of electric vehicles. When a lithium-ion power battery is used in an electric vehicle, the SOC displays a very strong time-dependent nonlinearity under the influence of random factors, such as the working conditions and the environment. Hence, research on estimating the SOC of a power battery for an electric vehicle is of great theoretical significance and application value. In this paper, according to the dynamic response of the power battery terminal voltage during a discharging process, the second-order RC circuit is first used as the equivalent model of the power battery. Subsequently, on the basis of this model, the least squares method (LS with a forgetting factor and the adaptive unscented Kalman filter (AUKF algorithm are used jointly in the estimation of the power battery SOC. Simulation experiments show that the joint estimation algorithm proposed in this paper has higher precision and convergence of the initial value error than a single AUKF algorithm.

  2. Series load induction heating inverter state estimator using Kalman filter

    Directory of Open Access Journals (Sweden)

    Szelitzky T.

    2011-12-01

    Full Text Available LQR and H2 controllers require access to the states of the controlled system. The method based on description function with Fourier series results in a model with immeasurable states. For this reason, we proposed a Kalman filter based state estimator, which not only filters the input signals, but also computes the unobservable states of the system. The algorithm of the filter was implemented in LabVIEW v8.6 and tested on recorded data obtained from a 10-40 kHz series load frequency controlled induction heating inverter.

  3. A new iterative speech enhancement scheme based on Kalman filtering

    DEFF Research Database (Denmark)

    Li, Chunjian; Andersen, Søren Vang

    2005-01-01

    Subtraction filter is introduced as an initialization procedure. Iterations are then made sequential inter-frame, exploiting the fact that the AR model changes slowly between neighboring frames. The proposed algorithm is computationally more efficient than a baseline EM algorithm due to its fast convergence...... for a high temporal resolution estimation of this variance. A Local Variance Estimator based on a Prediction Error Kalman Filter is designed for this high temporal resolution variance estimation. To achieve fast convergence and avoid local maxima of the likelihood function, a Weighted Power Spectral...

  4. Research on Kalman-filter based multisensor data fusion

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military theatre defense etc.Various multisensor data fusion methods have been extensively investigated by researchers,of which Klaman filtering is one of the most important.Kalman filtering is the best-known recursive least mean-square algorithm to optimally estimate the unknown.states of a dynamic system,which has found widespread application in many areas.The scope of the work is restricted to investigate the various data fusion and track fusion techniques based on the Kalman Filter methods.then a new method of state fusion is proposed.Finally the simulation results demonstrate the effectiveness of the introduced method.

  5. Observation Quality Control with a Robust Ensemble Kalman Filter

    KAUST Repository

    Roh, Soojin

    2013-12-01

    Current ensemble-based Kalman filter (EnKF) algorithms are not robust to gross observation errors caused by technical or human errors during the data collection process. In this paper, the authors consider two types of gross observational errors, additive statistical outliers and innovation outliers, and introduce a method to make EnKF robust to gross observation errors. Using both a one-dimensional linear system of dynamics and a 40-variable Lorenz model, the performance of the proposed robust ensemble Kalman filter (REnKF) was tested and it was found that the new approach greatly improves the performance of the filter in the presence of gross observation errors and leads to only a modest loss of accuracy with clean, outlier-free, observations.

  6. Kalman Filter Based Tracking in an Video Surveillance System

    OpenAIRE

    SULIMAN, C.; CRUCERU, C.; Moldoveanu, F.

    2010-01-01

    In this paper we have developed a Matlab/Simulink based model for monitoring a contact in a video surveillance sequence. For the segmentation process and corect identification of a contact in a surveillance video, we have used the Horn-Schunk optical flow algorithm. The position and the behavior of the correctly detected contact were monitored with the help of the traditional Kalman filter. After that we have compared the results obtained from the optical flow method with the ones obtaine...

  7. 基于渐消滤波的神经网络优化算法及其应用%Neural Network Learning Algorithm Based on Fading Kalman Filtering and Its Application

    Institute of Scientific and Technical Information of China (English)

    高社生; 杨一; 高兵兵

    2015-01-01

    针对BP神经网络在训练过程中易陷入局部极小值的问题,提出一种新的基于渐消滤波的神经网络学习算法。该算法采用渐消卡尔曼滤波对神经网络的权值进行更新,有效避免了梯度下降算法产生的局部极小问题;与卡尔曼滤波相比,在渐消滤波中充分考虑了最新量测值的影响,能更合理地利用新的有效信息,从而提高了学习算法的收敛速度。学习后的网络不仅具有普通神经网络的自主学习能力,而且具有渐消滤波的最优估计性能。将提出的神经网络算法应用于SINS/BDS组合导航系统进行仿真验证。结果表明,提出的算法在逼近精度方面优于BP算法和卡尔曼滤波算法,可以有效提高神经网络的泛化能力。%Aiming at the local minimum problem in the training process of BP neural network, this paper proposes a novel neural network learning algorithm based on the fading Kalman filtering. This algorithm avoids the problem of local minimum value because it provides the fading Kalman filtering for weights optimizing in neural network train⁃ing. Compared with Kalman filtering, the fading Kalman filtering gives full consideration to the effects of the newest measurements and uses the effective information more reasonably. Consequently, the convergence speed of the algo⁃rithm is improved. The network after training not only has the autonomous learning ability of general neural network, but also has optimal estimation performance of the fading Kalman filtering. The proposed algorithm is applied to SINS/BDS integrated navigation system. Simulation results and their analysis demonstrate preliminarily that the ap⁃proximation accuracy of the proposed learning algorithm is better than those of BP algorithm and Kalman filtering al⁃gorithm. The proposed algorithm, we believe, can improve the generalization capability of neural network effective⁃ly.

  8. Enhancement algorithm for infrared images based on Kalman filter%基于卡尔曼滤波的红外图像增强算法

    Institute of Scientific and Technical Information of China (English)

    刘涛; 赵巨峰; 徐之海; 冯华君; 陈慧芳

    2012-01-01

    针对红外图像中的非均匀性噪声的去除问题,提出基于卡尔曼滤波的红外图像去噪及增强算法.在Bayesian-MAP框架下分析卡尔曼滤波器对去噪问题的适用性.由于成像电路内部温度上升和参数的细微变化,每个像元的固定模式噪声(FPN)在帧间缓慢变化.基于此点,建立暗帧的噪声模型.将卡尔曼滤波器作用于红外暗帧序列,估计出暗帧中每个像元的FPN水平.引入噪声影响因子(NIF)来评估FPN噪声对像元输出信号的影响.根据NIF自适应地选取每个像元的FPN噪声权重.实际带噪图像减去加权FPN噪声,即得到增强图像.将该算法应用于实拍红外图像,用平均灰度梯度(GMG)评估算法的性能.在目标区域,GMG下降了5.1%,说明算法在去噪的同时很好地保留了目标的边缘.而在平滑区域,GMG下降了85.5%.结果表明,该算法在去除非均匀性噪声,提高图像的对比度方面,取得较好的效果.%To remove the non-uniformity noise in the infrared image, the denoise and enhancement algorithm based on Kalman filter is proposed. In the framework of Bayesian-MAP inference, the feasibility of Kalman filter was discussed. Due to the inner temperature increment and the circuit parameters' minor changes of the imaging system, each pixel's fixed pattern noise (FPN) varies slowly and non-uniformly between frames. Based on this view, the dark frame was modeled. The level of the FPN was estimated by Kalman filter acted on sequential dark frames. The noise influence factor (NIF) was introduced to evaluate the influence of the FPN to the pixel's output signal. And then, the reasonable weight of each pixel's FPN was determined adaptively by means of NIF. The non-uniformity of the infrared image can be corrected after weighted subtraction FPN from the real noisy image data on pixels one by one. The algorithm was applied in real infrared images and gray mean grads (GMG) was used to evaluate its performance. In

  9. Attitude determination and calibration using a recursive maximum likelihood-based adaptive Kalman filter

    Science.gov (United States)

    Kelly, D. A.; Fermelia, A.; Lee, G. K. F.

    1990-01-01

    An adaptive Kalman filter design that utilizes recursive maximum likelihood parameter identification is discussed. At the center of this design is the Kalman filter itself, which has the responsibility for attitude determination. At the same time, the identification algorithm is continually identifying the system parameters. The approach is applicable to nonlinear, as well as linear systems. This adaptive Kalman filter design has much potential for real time implementation, especially considering the fast clock speeds, cache memory and internal RAM available today. The recursive maximum likelihood algorithm is discussed in detail, with special attention directed towards its unique matrix formulation. The procedure for using the algorithm is described along with comments on how this algorithm interacts with the Kalman filter.

  10. A Recursive Kalman Filter Forecasting Approach

    OpenAIRE

    Douglas R. Kahl; Johannes Ledolter

    1983-01-01

    This paper examines the forecasting accuracy and the cost effectiveness of time series models with time-varying coefficients. A simulation study investigates the potential forecasting benefits of a proposed Kalman filter type adaptive estimation and forecasting approach. It is found that: (1) When appropriate, the time-varying coefficient approach leads to better forecasts than the constant coefficient procedures. (2) A simple decision rule, which indicates whether time-varying coefficient mo...

  11. The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model

    Science.gov (United States)

    Penny, S. G.; Kalnay, E.; Carton, J. A.; Hunt, B. R.; Ide, K.; Miyoshi, T.; Chepurin, G. A.

    2013-11-01

    The most widely used methods of data assimilation in large-scale oceanography, such as the Simple Ocean Data Assimilation (SODA) algorithm, specify the background error covariances and thus are unable to refine the weights in the assimilation as the circulation changes. In contrast, the more computationally expensive Ensemble Kalman Filters (EnKF) such as the Local Ensemble Transform Kalman Filter (LETKF) use an ensemble of model forecasts to predict changes in the background error covariances and thus should produce more accurate analyses. The EnKFs are based on the approximation that ensemble members reflect a Gaussian probability distribution that is transformed linearly during the forecast and analysis cycle. In the presence of nonlinearity, EnKFs can gain from replacing each analysis increment by a sequence of smaller increments obtained by recursively applying the forecast model and data assimilation procedure over a single analysis cycle. This has led to the development of the "running in place" (RIP) algorithm by Kalnay and Yang (2010) and Yang et al. (2012a,b) in which the weights computed at the end of each analysis cycle are used recursively to refine the ensemble at the beginning of the analysis cycle. To date, no studies have been carried out with RIP in a global domain with real observations. This paper provides a comparison of the aforementioned assimilation methods in a set of experiments spanning seven years (1997-2003) using identical forecast models, initial conditions, and observation data. While the emphasis is on understanding the similarities and differences between the assimilation methods, comparisons are also made to independent ocean station temperature, salinity, and velocity time series, as well as ocean transports, providing information about the absolute error of each. Comparisons to independent observations are similar for the assimilation methods but the observation-minus-background temperature differences are distinctly lower for

  12. Improved Kalman Filter Method for Measurement Noise Reduction in Multi Sensor RFID Systems

    Directory of Open Access Journals (Sweden)

    Min Chul Kim

    2011-10-01

    Full Text Available Recently, the range of available Radio Frequency Identification (RFID tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less Mean Squared Error (MSE than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments.

  13. Improved Kalman filter method for measurement noise reduction in multi sensor RFID systems.

    Science.gov (United States)

    Eom, Ki Hwan; Lee, Seung Joon; Kyung, Yeo Sun; Lee, Chang Won; Kim, Min Chul; Jung, Kyung Kwon

    2011-01-01

    Recently, the range of available radio frequency identification (RFID) tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less mean squared error (MSE) than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments.

  14. Modified unscented Kalman filter using modified filter gain and variance scale factor for highly maneuvering target tracking

    Institute of Scientific and Technical Information of China (English)

    Changyun Liu; Penglang Shui; Gang Wei; Song Li

    2014-01-01

    To improve the low tracking precision caused by lagged filter gain or imprecise state noise when the target highly maneu-vers, a modified unscented Kalman filter algorithm based on the improved filter gain and adaptive scale factor of state noise is pre-sented. In every filter process, the estimated scale factor is used to update the state noise covariance Qk, and the improved filter gain is obtained in the filter process of unscented Kalman filter (UKF) via predicted variance Pk|k-1, which is similar to the standard Kalman filter. Simulation results show that the proposed algorithm provides better accuracy and ability to adapt to the highly maneu-vering target compared with the standard UKF.

  15. A Multiresolution Ensemble Kalman Filter using Wavelet Decomposition

    CERN Document Server

    Hickmann, Kyle S

    2015-01-01

    We present a method of using classical wavelet based multiresolution analysis to separate scales in model and observations during data assimilation with the ensemble Kalman filter. In many applications, the underlying physics of a phenomena involve the interaction of features at multiple scales. Blending of observational and model error across scales can result in large forecast inaccuracies since large errors at one scale are interpreted as inexact data at all scales. Our method uses a transformation of the observation operator in order to separate the information from different scales of the observations. This naturally induces a transformation of the observation covariance and we put forward several algorithms to efficiently compute the transformed covariance. Another advantage of our multiresolution ensemble Kalman filter is that scales can be weighted independently to adjust each scale's effect on the forecast. To demonstrate feasibility we present applications to a one dimensional Kuramoto-Sivashinsky (...

  16. A Kalman Filtering Perspective for Multiatlas Segmentation.

    Science.gov (United States)

    Gao, Yi; Zhu, Liangjia; Cates, Joshua; MacLeod, Rob S; Bouix, Sylvain; Tannenbaum, Allen

    In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only computed by direct registration but also inferred from the transformation that aligns the previous atlas to the image together with the transformation between the two atlases. This process is similar to the global positioning system on a vehicle, which gets position by inquiring from the satellite and by employing the previous location and velocity-neither answer in isolation being perfect. To solve this problem, a dynamical system scheme is crucial to combine the two pieces of information; for example, a Kalman filtering scheme is used. Accordingly, in this work, a Kalman multiatlas segmentation is proposed to stabilize the global/affine registration step. The contributions of this work are twofold. First, it provides a new dynamical systematic perspective for standard independent multiatlas registrations, and it is solved by Kalman filtering. Second, with very little extra computation, it can be combined with most existing multiatlas segmentation schemes for better registration/segmentation accuracy.

  17. Autonomous Orbit Determination via Kalman Filtering of Gravity Gradients

    CERN Document Server

    Sun, Xiucong; Macabiau, Christophe; Han, Chao

    2016-01-01

    Spaceborne gravity gradients are proposed in this paper to provide autonomous orbit determination capabilities for near Earth satellites. The gravity gradients contain useful position information which can be extracted by matching the observations with a precise gravity model. The extended Kalman filter is investigated as the principal estimator. The stochastic model of orbital motion, the measurement equation and the model configuration are discussed for the filter design. An augmented state filter is also developed to deal with unknown significant measurement biases. Simulations are conducted to analyze the effects of initial errors, data-sampling periods, orbital heights, attitude and gradiometer noise levels, and measurement biases. Results show that the filter performs well with additive white noise observation errors. Degraded observability for the along-track position is found for the augmented state filter. Real flight data from the GOCE satellite are used to test the algorithm. Radial and cross-track...

  18. 基于RSSI的机器人室内卡尔曼滤波定位算法研究%Study of robot indoor Kalman filter positioning algorithm based on RSSI

    Institute of Scientific and Technical Information of China (English)

    安雷; 张国良; 汤文俊

    2012-01-01

    For the difficult problem of mobile robot positioning in indoor environment, this paper puts forward the Kalman filter positioning algorithm based on RSSI. The positioning algorithm based on RSSI is utilized to forecast the location of user, which the Kalman filter is used to optimize, in order to enhance the performance and stability of indoor positioning system. Experimental results show that the Kalman filter positioning algorithm, which has the robustness, can effectively improve the locational precision of indoor positioning system, and that the prospective objective is reached.%针对移动机器人在室内环境中定位难的问题,提出了一种基于RSSI(Receive Signal Strength Indicator)的卡尔曼滤波定位算法.利用基于RSSI的定位方法估算用户的位置坐标,利用卡尔曼滤波算法对用户的估算位置坐标进行优化处理,以提高室内定位系统的性能和稳定性.实验结果表明,卡尔曼滤波算法是鲁棒的,可以有效改善系统的定位精度,达到了预期的目的.

  19. Estimation of noise parameters in dynamical system identification with Kalman filters.

    Science.gov (United States)

    Kwasniok, Frank

    2012-09-01

    A method is proposed for determining dynamical and observational noise parameters in state and parameter identification from time series using Kalman filters. The noise covariances are estimated in a secondary optimization by maximizing the predictive likelihood of the data. The approach is based on internal consistency; for the correct noise parameters, the uncertainty projected by the Kalman filter matches the actual predictive uncertainty. The method is able to disentangle dynamical and observational noise. The algorithm is demonstrated for the linear, extended, and unscented Kalman filters using an Ornstein-Uhlenbeck process, the noise-driven Lorenz system, and van der Pol oscillator as well as a paleoclimatic ice-core record as examples. The approach is also applicable to the ensemble Kalman filter and can be readily extended to non-Gaussian estimation frameworks such as Gaussian-sum filters and particle filters.

  20. Deterministic Mean-Field Ensemble Kalman Filtering

    KAUST Repository

    Law, Kody J. H.

    2016-05-03

    The proof of convergence of the standard ensemble Kalman filter (EnKF) from Le Gland, Monbet, and Tran [Large sample asymptotics for the ensemble Kalman filter, in The Oxford Handbook of Nonlinear Filtering, Oxford University Press, Oxford, UK, 2011, pp. 598--631] is extended to non-Gaussian state-space models. A density-based deterministic approximation of the mean-field limit EnKF (DMFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence k between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for dimension d<2k. The fidelity of approximation of the true distribution is also established using an extension of the total variation metric to random measures. This is limited by a Gaussian bias term arising from nonlinearity/non-Gaussianity of the model, which arises in both deterministic and standard EnKF. Numerical results support and extend the theory.

  1. 基于Sage-Husa的线性自适应平方根卡尔曼滤波算法%A Novel Algorithm of Linear Adaptive Square-Root Kalman Filtering Based on Sage-Husa

    Institute of Scientific and Technical Information of China (English)

    周勇; 张玉峰; 张超; 张举中

    2013-01-01

    Aiming at the flaws of the standard Kalman Filter(KF) and Extended Kalman Filter (EKF) , and based on the square-root filtering algorithm, we modify traditional Sage-Husa adaptive filter and present a novel algorithm of Linear Adaptive Square-Root Kalman Filtering( LASRKF) in this paper. With this new filter, the square root of system state covariance matrix is calculated recursively and the estimation of the square root of the system noise co-variance matrix is obtained straightforwardly. Then the positive semi-definiteness of system state and noise covariance matrix are guaranteed; the stability and the adaptability of filter are also enhanced. Compared with the traditional Sage-Husa adaptive filtering algorithm, LASRKF algorithm improves the anti-divergence capability. Simulation results show preliminarily that the stability, accuracy and adaptability of the filter are improved greatly.%针对标准卡尔曼滤波和扩展卡尔曼滤波存在的局限性,结合平方根滤波的思想,对传统Sage-Husa估计器进行改进,提出了一种新的线性自适应平方根卡尔曼滤波(Linear Adaptive Square-Root Kalman Filtering,LASRKF)算法.该算法直接对系统状态方差阵和噪声方差阵的平方根进行递推与估算,确保了状态和噪声方差阵的对称性和非负定性;算法还增添了对系统噪声统计特性估计的计算,强化了滤波器的稳定性和自适应能力;与传统Sage-Husa自适应滤波算法相比LASRKF可提高滤波器抗发散的能力.仿真实验表明,LASRKF可有效提高滤波器的精确性、稳定性和自适应能力.

  2. An introduction to Kalman filtering with Matlab examples

    CERN Document Server

    Kovvali, Narayan; Spanias, Andreas

    2013-01-01

    The Kalman filter is the Bayesian optimum solution to the problem of sequentially estimating the states of a dynamical system in which the state evolution and measurement processes are both linear and Gaussian. Given the ubiquity of such systems, the Kalman filter finds use in a variety of applications, e.g., target tracking, guidance and navigation, and communications systems. The purpose of this book is to present a brief introduction to Kalman filtering. The theoretical framework of the Kalman filter is first presented, followed by examples showing its use in practical applications. Extensi

  3. Reservoir History Matching Using Ensemble Kalman Filters with Anamorphosis Transforms

    KAUST Repository

    Aman, Beshir M.

    2012-12-01

    This work aims to enhance the Ensemble Kalman Filter performance by transforming the non-Gaussian state variables into Gaussian variables to be a step closer to optimality. This is done by using univariate and multivariate Box-Cox transformation. Some History matching methods such as Kalman filter, particle filter and the ensemble Kalman filter are reviewed and applied to a test case in the reservoir application. The key idea is to apply the transformation before the update step and then transform back after applying the Kalman correction. In general, the results of the multivariate method was promising, despite the fact it over-estimated some variables.

  4. Detection of Harmonic Occurring using Kalman Filtering

    DEFF Research Database (Denmark)

    Hussain, Dil Muhammad Akbar; Shoro, Ghulam Mustafa; Imran, Raja Muhammed

    2014-01-01

    As long as the load to a power system is linear which has been the case before 80's, typically no harmonics are produced. However, the modern power electronic equipment for controlled power consumption produces harmonic disturbances, these devices/equipment possess nonlinear voltage/current chara...... using Kalman filter. This may be very useful for example to quickly switching on certain filters based on the harmonic present. We are using a unique technique to detect the occurrence of harmonics......./current characteristic. These harmonics are not to be allowed to grow beyond a certain limit to avoid any grave consequence to the customer’s main supply. Filters can be implemented at the power source or utility location to eliminate these harmonics. In this paper we detect the instance at which these harmonics occur...

  5. IAE-adaptive Kalman filter for INS/GPS integrated navigation system

    Institute of Scientific and Technical Information of China (English)

    Bian Hongwei; Jin Zhihua; Tian Weifeng

    2006-01-01

    A marine INS/GPS adaptive navigation system is presented in this paper. GPS with two antenna providing vessel's altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter (SKF) assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.

  6. Ensemble-based Kalman Filters in Strongly Nonlinear Dynamics

    Institute of Scientific and Technical Information of China (English)

    Zhaoxia PU; Joshua HACKER

    2009-01-01

    This study examines the effectiveness of ensemble Kalman filters in data assimilation with the strongly nonlinear dynamics of the Lorenz-63 model, and in particular their use in predicting the regime transition that occurs when the model jumps from one basin of attraction to the other. Four configurations of the ensemble-based Kalman filtering data assimilation techniques, including the ensemble Kalman filter, ensemble adjustment Kalman filter, ensemble square root filter and ensemble transform Kalman filter, are evaluated with their ability in predicting the regime transition (also called phase transition) and also are compared in terms of their sensitivity to both observational and sampling errors. The sensitivity of each ensemble-based filter to the size of the ensemble is also examined.

  7. 基于卡尔曼滤波算法的MEMS陀螺仪误差补偿研究%Drift Compensation of MEMS Gyro with Kalman Filtering Algorithm

    Institute of Scientific and Technical Information of China (English)

    徐韩; 曾超; 黄清华

    2016-01-01

    Aiming at solving the error of MEMS gyroscope’s drift in the test,the kalman algorithm was proposed. In the thesis,MPU3050 gyroscope was analyzed,and its drift model was proposed. After the analysis of the cause of the error of the gyroscope,the error model of the gyroscope was established. The effect of kalman filter was evaluat⁃ed on the Allan variance method. Compare with other filtering algorithm,kalman filtering algorithm which can re⁃duce the burden of SCM(Single chip microcomputer)with a little computation,and the gyroscopes were tested on the rotating speed device. The experimental results show that the combination of the error of model and kalman fil⁃ter algorithm can reduce drift of the gyroscope in the test.%为了补偿MEMS陀螺仪的漂移误差,本文采用了一种新的卡尔曼滤波算法。文中对MPU3050陀螺仪进行分析,提出其漂移模型。通过分析陀螺仪的误差,建立陀螺仪的误差模型。卡尔曼滤波的效果通过阿伦方差进行评估,与其他滤波算法比较,在MEMS陀螺仪中采用卡尔曼滤波算法可以有效的减少SCM(单片机)计算,并在转台上对陀螺仪进行测试。实验结果表明,结合误差模型和卡尔曼滤波算法可有有效的减少陀螺仪的漂移误差。

  8. Filtering Meteoroid Flights Using Multiple Unscented Kalman Filters

    Science.gov (United States)

    Sansom, E. K.; Bland, P. A.; Rutten, M. G.; Paxman, J.; Towner, M. C.

    2016-11-01

    Estimator algorithms are immensely versatile and powerful tools that can be applied to any problem where a dynamic system can be modeled by a set of equations and where observations are available. A well designed estimator enables system states to be optimally predicted and errors to be rigorously quantified. Unscented Kalman filters (UKFs) and interactive multiple models can be found in methods from satellite tracking to self-driving cars. The luminous trajectory of the Bunburra Rockhole fireball was observed by the Desert Fireball Network in mid-2007. The recorded data set is used in this paper to examine the application of these two techniques as a viable approach to characterizing fireball dynamics. The nonlinear, single-body system of equations, used to model meteoroid entry through the atmosphere, is challenged by gross fragmentation events that may occur. The incorporation of the UKF within an interactive multiple model smoother provides a likely solution for when fragmentation events may occur as well as providing a statistical analysis of the state uncertainties. In addition to these benefits, another advantage of this approach is its automatability for use within an image processing pipeline to facilitate large fireball data analyses and meteorite recoveries.

  9. A new Recommender system based on target tracking: a Kalman Filter approach

    CERN Document Server

    Nowakowski, Samuel; Boyer, Anne

    2010-01-01

    In this paper, we propose a new approach for recommender systems based on target tracking by Kalman filtering. We assume that users and their seen resources are vectors in the multidimensional space of the categories of the resources. Knowing this space, we propose an algorithm based on a Kalman filter to track users and to predict the best prediction of their future position in the recommendation space.

  10. Target tracking in the recommender space: Toward a new recommender system based on Kalman filtering

    CERN Document Server

    Nowakowski, Samuel; Boyer, Anne

    2010-01-01

    In this paper, we propose a new approach for recommender systems based on target tracking by Kalman filtering. We assume that users and their seen resources are vectors in the multidimensional space of the categories of the resources. Knowing this space, we propose an algorithm based on a Kalman filter to track users and to predict the best prediction of their future position in the recommendation space.

  11. Target Centroid Position Estimation of Phase-Path Volume Kalman Filtering

    Directory of Open Access Journals (Sweden)

    Fengjun Hu

    2016-01-01

    Full Text Available For the problem of easily losing track target when obstacles appear in intelligent robot target tracking, this paper proposes a target tracking algorithm integrating reduced dimension optimal Kalman filtering algorithm based on phase-path volume integral with Camshift algorithm. After analyzing the defects of Camshift algorithm, compare the performance with the SIFT algorithm and Mean Shift algorithm, and Kalman filtering algorithm is used for fusion optimization aiming at the defects. Then aiming at the increasing amount of calculation in integrated algorithm, reduce dimension with the phase-path volume integral instead of the Gaussian integral in Kalman algorithm and reduce the number of sampling points in the filtering process without influencing the operational precision of the original algorithm. Finally set the target centroid position from the Camshift algorithm iteration as the observation value of the improved Kalman filtering algorithm to fix predictive value; thus to make optimal estimation of target centroid position and keep the target tracking so that the robot can understand the environmental scene and react in time correctly according to the changes. The experiments show that the improved algorithm proposed in this paper shows good performance in target tracking with obstructions and reduces the computational complexity of the algorithm through the dimension reduction.

  12. Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

    Directory of Open Access Journals (Sweden)

    Xi Liu

    2016-09-01

    Full Text Available A new algorithm called maximum correntropy unscented Kalman filter (MCUKF is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC, the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm.

  13. Algorithme d'adaptation du filtre de Kalman aux variations soudaines de bruit

    Science.gov (United States)

    Canciu, Vintila

    This research targets the case of Kalman filtering as applied to linear time-invariant systems having unknown process noise covariance and measurement noise covariance matrices and addresses the problem represented by the incomplete a priori knowledge of these two filter initialization parameters. The goal of this research is to determine in realtime both the process covariance matrix and the noise covariance matrix in the context of adaptive Kalman filtering. The resultant filter, called evolutionary adaptive Kalman filter, is able to adapt to sudden noise variations and constitutes a hybrid solution for adaptive Kalman filtering based on metaheuristic algorithms. MATLAB/Simulink simulation using several processes and covariance matrices plus comparison with other filters was selected as validation method. The Cramer-Rae Lower Bound (CRLB) was used as performance criterion. The thesis begins with a description of the problem under consideration (the design of a Kalman filter that is able to adapt to sudden noise variations) followed by a typical application (INS-GPS integrated navigation system) and by a statistical analysis of publications related to adaptive Kalman filtering. Next, the thesis presents the current architectures of the adaptive Kalman filtering: the innovation adaptive estimator (IAE) and the multiple model adaptive estimator (MMAE). It briefly presents their formulation, their behavior, and the limit of their performances. The thesis continues with the architectural synthesis of the evolutionary adaptive Kalman filter. The steps involved in the solution of the problem under consideration is also presented: an analysis of Kalman filtering and sub-optimal filtering methods, a comparison of current adaptive Kalman and sub-optimal filtering methods, the emergence of evolutionary adaptive Kalman filter as an enrichment of sub-optimal filtering with the help of biological-inspired computational intelligence methods, and the step-by-step architectural

  14. Vehicle Sideslip Angle Estimation Based on Hybrid Kalman Filter

    Directory of Open Access Journals (Sweden)

    Jing Li

    2016-01-01

    Full Text Available Vehicle sideslip angle is essential for active safety control systems. This paper presents a new hybrid Kalman filter to estimate vehicle sideslip angle based on the 3-DoF nonlinear vehicle dynamic model combined with Magic Formula tire model. The hybrid Kalman filter is realized by combining square-root cubature Kalman filter (SCKF, which has quick convergence and numerical stability, with square-root cubature based receding horizon Kalman FIR filter (SCRHKF, which has robustness against model uncertainty and temporary noise. Moreover, SCKF and SCRHKF work in parallel, and the estimation outputs of two filters are merged by interacting multiple model (IMM approach. Experimental results show the accuracy and robustness of the hybrid Kalman filter.

  15. SIMULASI FILTER KALMAN UNTUK ESTIMASI SUDUT DENGAN MENGGUNAKAN SENSOR GYROSCOPE

    Directory of Open Access Journals (Sweden)

    Wahyudi Wahyudi

    2012-02-01

    Full Text Available The Kalman filter is a recursive solution to the process linear filtering problem that can remove the noisefrom signal and then the information can useful. The process that use Kalman filter must be approximatedas two equations of linear system, state equation and output equation. Computation of Kalman filter isminimizes the mean of the square error. This paper explore the basic consepts of the Kalman filteralgorithm and simulate its to filter data of gyroscope to get a rotation. The measurement noise covariancedetermines how much information from the sample is used. If measurement noise covariance is high showthat the measurement isn’t very accurate. The process noise covariance contributes to the overalluncertainty of the estimate as it is added to the error covariance matrix in each time step. If the errorcovariance matrix is small the Kalman filter incorporates a lot less of the measurement into estimate ofrotation.

  16. Diffusion Strategies for Distributed Kalman Filter with Dynamic Topologies in Virtualized Sensor Networks

    Directory of Open Access Journals (Sweden)

    Shujie Yang

    2016-01-01

    Full Text Available Network virtualization has become pervasive and is used in many applications. Through the combination of network virtualization and wireless sensor networks, it can greatly improve the multiple applications of traditional wireless sensor networks. However, because of the dynamic reconfiguration of topologies in the physical layer of virtualized sensor networks (VSNs, it requires a mechanism to guarantee the accuracy of estimate values by sensors. In this paper, we focus on the distributed Kalman filter algorithm with dynamic topologies to support this requirement. As one strategy of distributed Kalman filter algorithms, diffusion Kalman filter algorithm has a better performance on the state estimation. However, the existing diffusion Kalman filter algorithms all focus on the fixed topologies. Considering the dynamic topologies in the physical layer of VSNs mentioned above, we present a diffusion Kalman filter algorithm with dynamic topologies (DKFdt. Then, we emphatically derive the theoretical expressions of the mean and mean-square performance. From the expressions, the feasibility of the algorithm is verified. Finally, simulations confirm that the proposed algorithm achieves a greatly improved performance as compared with a noncooperative manner.

  17. VLBI TRF determination via Kalman filtering

    Science.gov (United States)

    Soja, Benedikt; Karbon, Maria; Nilsson, Tobias; Glaser, Susanne; Balidakis, Kyriakos; Heinkelmann, Robert; Schuh, Harald

    2015-04-01

    The determination of station positions is one of the primary tasks for space geodetic techniques. Station coordinate offsets are usually determined with respect to a linear coordinate model after removing elastic displacements caused by mass redistributions within the Earth's system. In operational VLBI analysis, the coordinate offsets are estimated in a least-squares adjustment as a constant over the duration of a 24-hour VLBI experiment. Terrestrial reference frames (TRF) are usually derived by adjusting the normal equations that contain the 24-hour constant offsets in order to estimate a linear model, possibly including breaks, for the station positions. We have created a VLBI TRF solution without the assumption of negligible subdaily motion and of linear behavior on longer time scales by applying a Kalman filter. As a preparation for the upcoming VLBI Global Observing System (VGOS), which aims for continuous observations that are available in real-time, a Kalman filter has been implemented into the VLBI software VieVS@GFZ. In addition to the real-time capability, the filter offers the possibility of stochastically modeling the parameters of interest. For station coordinates, changes in a subdaily time frame occur, for instance, from un- or mismodeled geophysical effects. The models for tidal and non-tidal ocean, atmosphere, and hydrology loading are known to have deficiencies and inconsistencies which propagate into the estimated station coordinates. The stochastic model of the Kalman filter can be adapted to take these subdaily effects into account. Comparing the resulting station coordinate time series with daily values from a least squares fit, we have investigated to what extent and in which regions the loading models currently have deficiencies. Due to the high correlation between station height and tropospheric delays, it is possible that errors in one group of parameters are partly absorbed by the other group. To detect problems with correlations and to

  18. 船舶交通服务系统雷达网误差配准算法%Registration Algorithm of VTS Radar Network System Based on Square-root Kalman Filter

    Institute of Scientific and Technical Information of China (English)

    肖进丽; 刘明俊; 刘克中

    2012-01-01

    在船舶交通服务系统(Vessel Traffic Services,VTS)利用多台雷达组成的雷达网中,如果雷达的系统误差未经配准就进行多雷达数据融合,则会使融合结果不可信而严重影响其航迹跟踪质量.平方根无味卡尔曼滤波 (Square-root Unscented Kalman Filter,SRUKF)是一种改进的无味卡尔曼滤波(Unscented Kalman Filter,UKF)算法,它借鉴了平方根卡尔曼滤波(Square-root Kalman Filter,SRKF)能克服滤波发散的思想来设计滤波器,不仅具备无味卡尔曼滤波的全部优点,而且克服了无味卡尔曼滤波由于滤波数值计算中舍入误差的积累而容易导致协方差矩阵失去非负定性的缺点,具有更好的数值稳定性.利用平方根无味卡尔曼滤波实现船舶交通服务系统中的雷达网系统误差配准,并通过Matlab仿真对该方法和无味卡尔曼滤波的滤波性能进行了比较,仿真结果验证了该方法的可行性和有效性.%In multi-radar network of VTS, to improve the reliability and quality of target tracking through multiple radar data fusion, it is necessary to register system errors of the radars. Since Square-root Unscented Kalman Filter ( SRUKF) , a modified filtering algorithm based on Unscented Kalman Filter (UKF) , has higher estimation precision and better filtering stability Compared with UKF, it is introduced for error registration of VTS radar networks. Matlab simulation results validated the algorithm.

  19. Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

    Directory of Open Access Journals (Sweden)

    Romy Budhi Widodo

    2016-01-01

    Full Text Available Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1 the KF-based model without compensating for external acceleration, (2 the proposed KF-based model which employs the external acceleration compensation model, (3 the two-step KF using weighted-based switching approach, (4 the KF-based model which uses the threshold-based approach, and (5 the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.

  20. An iterative ensemble Kalman filter for reservoir engineering applications

    NARCIS (Netherlands)

    Krymskaya, M.V.; Hanea, R.G.; Verlaan, M.

    2009-01-01

    The study has been focused on examining the usage and the applicability of ensemble Kalman filtering techniques to the history matching procedures. The ensemble Kalman filter (EnKF) is often applied nowadays to solving such a problem. Meanwhile, traditional EnKF requires assumption of the

  1. An iterative ensemble Kalman filter for reservoir engineering applications

    NARCIS (Netherlands)

    Krymskaya, M.V.; Hanea, R.G.; Verlaan, M.

    2009-01-01

    The study has been focused on examining the usage and the applicability of ensemble Kalman filtering techniques to the history matching procedures. The ensemble Kalman filter (EnKF) is often applied nowadays to solving such a problem. Meanwhile, traditional EnKF requires assumption of the distributi

  2. Statistical Process Control of a Kalman Filter Model

    Science.gov (United States)

    Gamse, Sonja; Nobakht-Ersi, Fereydoun; Sharifi, Mohammad A.

    2014-01-01

    For the evaluation of measurement data, different functional and stochastic models can be used. In the case of time series, a Kalman filtering (KF) algorithm can be implemented. In this case, a very well-known stochastic model, which includes statistical tests in the domain of measurements and in the system state domain, is used. Because the output results depend strongly on input model parameters and the normal distribution of residuals is not always fulfilled, it is very important to perform all possible tests on output results. In this contribution, we give a detailed description of the evaluation of the Kalman filter model. We describe indicators of inner confidence, such as controllability and observability, the determinant of state transition matrix and observing the properties of the a posteriori system state covariance matrix and the properties of the Kalman gain matrix. The statistical tests include the convergence of standard deviations of the system state components and normal distribution beside standard tests. Especially, computing controllability and observability matrices and controlling the normal distribution of residuals are not the standard procedures in the implementation of KF. Practical implementation is done on geodetic kinematic observations. PMID:25264959

  3. Statistical Process Control of a Kalman Filter Model

    Directory of Open Access Journals (Sweden)

    Sonja Gamse

    2014-09-01

    Full Text Available For the evaluation of measurement data, different functional and stochastic models can be used. In the case of time series, a Kalman filtering (KF algorithm can be implemented. In this case, a very well-known stochastic model, which includes statistical tests in the domain of measurements and in the system state domain, is used. Because the output results depend strongly on input model parameters and the normal distribution of residuals is not always fulfilled, it is very important to perform all possible tests on output results. In this contribution, we give a detailed description of the evaluation of the Kalman filter model. We describe indicators of inner confidence, such as controllability and observability, the determinant of state transition matrix and observing the properties of the a posteriori system state covariance matrix and the properties of the Kalman gain matrix. The statistical tests include the convergence of standard deviations of the system state components and normal distribution beside standard tests. Especially, computing controllability and observability matrices and controlling the normal distribution of residuals are not the standard procedures in the implementation of KF. Practical implementation is done on geodetic kinematic observations.

  4. Statistical process control of a Kalman filter model.

    Science.gov (United States)

    Gamse, Sonja; Nobakht-Ersi, Fereydoun; Sharifi, Mohammad A

    2014-09-26

    For the evaluation of measurement data, different functional and stochastic models can be used. In the case of time series, a Kalman filtering (KF) algorithm can be implemented. In this case, a very well-known stochastic model, which includes statistical tests in the domain of measurements and in the system state domain, is used. Because the output results depend strongly on input model parameters and the normal distribution of residuals is not always fulfilled, it is very important to perform all possible tests on output results. In this contribution, we give a detailed description of the evaluation of the Kalman filter model. We describe indicators of inner confidence, such as controllability and observability, the determinant of state transition matrix and observing the properties of the a posteriori system state covariance matrix and the properties of the Kalman gain matrix. The statistical tests include the convergence of standard deviations of the system state components and normal distribution beside standard tests. Especially, computing controllability and observability matrices and controlling the normal distribution of residuals are not the standard procedures in the implementation of KF. Practical implementation is done on geodetic kinematic observations.

  5. Local Ensemble Kalman Particle Filters for efficient data assimilation

    CERN Document Server

    Robert, Sylvain

    2016-01-01

    Ensemble methods such as the Ensemble Kalman Filter (EnKF) are widely used for data assimilation in large-scale geophysical applications, as for example in numerical weather prediction (NWP). There is a growing interest for physical models with higher and higher resolution, which brings new challenges for data assimilation techniques because of the presence of non-linear and non-Gaussian features that are not adequately treated by the EnKF. We propose two new localized algorithms based on the Ensemble Kalman Particle Filter (EnKPF), a hybrid method combining the EnKF and the Particle Filter (PF) in a way that maintains scalability and sample diversity. Localization is a key element of the success of EnKFs in practice, but it is much more challenging to apply to PFs. The algorithms that we introduce in the present paper provide a compromise between the EnKF and the PF while avoiding some of the problems of localization for pure PFs. Numerical experiments with a simplified model of cumulus convection based on a...

  6. Analyses of integrated aircraft cabin contaminant monitoring network based on Kalman consensus filter.

    Science.gov (United States)

    Wang, Rui; Li, Yanxiao; Sun, Hui; Chen, Zengqiang

    2017-07-11

    The modern civil aircrafts use air ventilation pressurized cabins subject to the limited space. In order to monitor multiple contaminants and overcome the hypersensitivity of the single sensor, the paper constructs an output correction integrated sensor configuration using sensors with different measurement theories after comparing to other two different configurations. This proposed configuration works as a node in the contaminant distributed wireless sensor monitoring network. The corresponding measurement error models of integrated sensors are also proposed by using the Kalman consensus filter to estimate states and conduct data fusion in order to regulate the single sensor measurement results. The paper develops the sufficient proof of the Kalman consensus filter stability when considering the system and the observation noises and compares the mean estimation and the mean consensus errors between Kalman consensus filter and local Kalman filter. The numerical example analyses show the effectiveness of the algorithm. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  7. Non-linear Kalman filters for calibration in radio interferometry

    CERN Document Server

    Tasse, Cyril

    2014-01-01

    We present a new calibration scheme based on a non-linear version of Kalman filter that aims at estimating the physical terms appearing in the Radio Interferometry Measurement Equation (RIME). We enrich the filter's structure with a tunable data representation model, together with an augmented measurement model for regularization. We show using simulations that it can properly estimate the physical effects appearing in the RIME. We found that this approach is particularly useful in the most extreme cases such as when ionospheric and clock effects are simultaneously present. Combined with the ability to provide prior knowledge on the expected structure of the physical instrumental effects (expected physical state and dynamics), we obtain a fairly cheap algorithm that we believe to be robust, especially in low signal-to-noise regime. Potentially the use of filters and other similar methods can represent an improvement for calibration in radio interferometry, under the condition that the effects corrupting visib...

  8. Secure Tracking in Sensor Networks using Adaptive Extended Kalman Filter

    CERN Document Server

    Fard, Ali P

    2012-01-01

    Location information of sensor nodes has become an essential part of many applications in Wireless Sensor Networks (WSN). The importance of location estimation and object tracking has made them the target of many security attacks. Various methods have tried to provide location information with high accuracy, while lots of them have neglected the fact that WSNs may be deployed in hostile environments. In this paper, we address the problem of securely tracking a Mobile Node (MN) which has been noticed very little previously. A novel secure tracking algorithm is proposed based on Extended Kalman Filter (EKF) that is capable of tracking a Mobile Node (MN) with high resolution in the presence of compromised or colluding malicious beacon nodes. It filters out and identifies the malicious beacon data in the process of tracking. The proposed method considerably outperforms the previously proposed secure algorithms in terms of either detection rate or MSE. The experimental data based on different settings for the netw...

  9. Kalman Filter for Spinning Spacecraft Attitude Estimation

    Science.gov (United States)

    Markley, F. Landis; Sedlak, Joseph E.

    2008-01-01

    This paper presents a Kalman filter using a seven-component attitude state vector comprising the angular momentum components in an inertial reference frame, the angular momentum components in the body frame, and a rotation angle. The relatively slow variation of these parameters makes this parameterization advantageous for spinning spacecraft attitude estimation. The filter accounts for the constraint that the magnitude of the angular momentum vector is the same in the inertial and body frames by employing a reduced six-component error state. Four variants of the filter, defined by different choices for the reduced error state, are tested against a quaternion-based filter using simulated data for the THEMIS mission. Three of these variants choose three of the components of the error state to be the infinitesimal attitude error angles, facilitating the computation of measurement sensitivity matrices and causing the usual 3x3 attitude covariance matrix to be a submatrix of the 6x6 covariance of the error state. These variants differ in their choice for the other three components of the error state. The variant employing the infinitesimal attitude error angles and the angular momentum components in an inertial reference frame as the error state shows the best combination of robustness and efficiency in the simulations. Attitude estimation results using THEMIS flight data are also presented.

  10. Orchard navigation using derivative free Kalman filtering

    DEFF Research Database (Denmark)

    Hansen, Søren; Bayramoglu, Enis; Andersen, Jens Christian

    2011-01-01

    This paper describes the use of derivative free filters for mobile robot localization and navigation in an orchard. The localization algorithm fuses odometry and gyro measurements with line features representing the surrounding fruit trees of the orchard. The line features are created on basis of 2......D laser scanner data by a least square algorithm. The three derivative free filters are compared to an EKF based localization method on a typical run covering four rows in the orchard. The Matlab R toolbox Kalmtool is used for easy switching between different filter implementations without the need...

  11. 方位捷联平台重力仪分布式Kalman滤波初始对准算法%Distributed Kalman filter initial alignment algorithm for azimuth strapdown platform gravimeter

    Institute of Scientific and Technical Information of China (English)

    杨晔; 毋兴涛; 杨建林; 高巍; 裴志

    2014-01-01

    为充分利用分布式架构重力仪各处理器并行计算的能力,解决单个处理器运行整体式 Kalman滤波所遇到的非实时性问题,设计了一种分布式 Kalman 滤波对准算法。首先,给出了方位捷联平台重力仪的误差方程,建立了系统的状态方程和观测方程。然后,用协方差分析法对系统初始对准滤波方程进行处理,将原系统分解成维数相同的两个子系统,得到由两个子滤波器构成的初始对准滤波器。最后,利用Matlab建立了方位捷联平台惯导模型,分别应用整体式滤波和分布式滤波进行静基座初始对准。仿真结果表明,分布式滤波算法与整体式滤波算法具有相同的滤波精度,并且分布式滤波用时只有整体式滤波的60%,更有利于保证滤波算法的实时性。%A distributed Kalman filter alignment algorithm is developed in order to use the parallel computing ability of the distributed architecture gravimeter to solve the non real-time implementation of filtering based on one single processor. Firstly, the error equations of the azimuth strapdown platform gravimeter are deduced, and the state equations and observation equations are built. Secondly, an error covariance analytical method is applied to the filtering equations, and the system is decentralised into two subsystems with the same dimension. In this way we get the initial alignment filter formed by the two subfilters. Finally, the azimuth strapdown platform model is built by using Matlab, and stationary base alignment is implemented by using global Kalman filter and distributed Kalman filter separately. The simulation results show that the distributed filter has the same filtering accuracy and costs only 60%of time compared with the global one, which is favorable to ensure the real-time performance of the algorithm.

  12. Dynamic Optimization of Feedforward Automatic Gauge Control Based on Extended Kalman Filter

    Institute of Scientific and Technical Information of China (English)

    YANG Bin-hu; YANG Wei-dong; CHEN Lian-gui; QU Lei

    2008-01-01

    Automatic gauge control is an essentially nonlinear process varying with time delay, and stochastically varying input and process noise always influence the target gauge control accuracy. To improve the control capability of feedforward automatic gauge control, Kalman filter was employed to filter the noise signal transferred from one stand to another. The linearized matrix that the Kalman filter algorithm needed was concluded; thus, the feedforward automatic gauge control architecture was dynamically optimized. The theoretical analyses and simulation show that the proposed algorithm is reasonable and effective.

  13. Study of the algorithm of backtracking decoupling and adaptive extended Kalman filter based on the quaternion expanded to the state variable for underwater glider navigation.

    Science.gov (United States)

    Huang, Haoqian; Chen, Xiyuan; Zhou, Zhikai; Xu, Yuan; Lv, Caiping

    2014-12-03

    High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.

  14. A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

    Science.gov (United States)

    Celaya, Jose R.; Saxen, Abhinav; Goebel, Kai

    2012-01-01

    This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

  15. Optimized object tracking technique using Kalman filter

    Directory of Open Access Journals (Sweden)

    Liana Ellen Taylor

    2016-07-01

    Full Text Available This paper focused on the design of an optimized object tracking technique which would minimize the processing time required in the object detection process while maintaining accuracy in detecting the desired moving object in a cluttered scene. A Kalman filter based cropped image is used for the image detection process as the processing time is significantly less to detect the object when a search window is used that is smaller than the entire video frame. This technique was tested with various sizes of the window in the cropping process. MATLAB® was used to design and test the proposed method. This paper found that using a cropped image with 2.16 multiplied by the largest dimension of the object resulted in significantly faster processing time while still providing a high success rate of detection and a detected center of the object that was reasonably close to the actual center.

  16. Computationally efficient video restoration for Nyquist sampled imaging sensors combining an affine-motion-based temporal Kalman filter and adaptive Wiener filter.

    Science.gov (United States)

    Rucci, Michael; Hardie, Russell C; Barnard, Kenneth J

    2014-05-01

    In this paper, we present a computationally efficient video restoration algorithm to address both blur and noise for a Nyquist sampled imaging system. The proposed method utilizes a temporal Kalman filter followed by a correlation-model based spatial adaptive Wiener filter (AWF). The Kalman filter employs an affine background motion model and novel process-noise variance estimate. We also propose and demonstrate a new multidelay temporal Kalman filter designed to more robustly treat local motion. The AWF is a spatial operation that performs deconvolution and adapts to the spatially varying residual noise left in the Kalman filter stage. In image areas where the temporal Kalman filter is able to provide significant noise reduction, the AWF can be aggressive in its deconvolution. In other areas, where less noise reduction is achieved with the Kalman filter, the AWF balances the deconvolution with spatial noise reduction. In this way, the Kalman filter and AWF work together effectively, but without the computational burden of full joint spatiotemporal processing. We also propose a novel hybrid system that combines a temporal Kalman filter and BM3D processing. To illustrate the efficacy of the proposed methods, we test the algorithms on both simulated imagery and video collected with a visible camera.

  17. State-Of-Charge Estimation of Li-Ion Battery Using Extended Kalman Filter

    Directory of Open Access Journals (Sweden)

    Feng Jin

    2013-07-01

    Full Text Available The Li-ion battery is studied base on its equivalent circuit PNGV model. The model parameters are identified by HPPC test. The discrete state space equation is established according to the model. The basic theory of extended Kalman filter algorithm is studied and then the filtering algorithm is set up under the noisy environments. Finally, a kind of electric car is used for testing under the UDDS driving condition. The difference between the simulation value using extended Kalman filter under the noisy environment and the theoretical value is compared. The result indicated that the extended Kalman filter keeps an excellent precision in state of charge estimation of Li-ion battery and performs well when disturbance happens.

  18. The Reduced Rank of Ensemble Kalman Filter to Estimate the Temperature of Non Isothermal Continue Stirred Tank Reactor

    Directory of Open Access Journals (Sweden)

    Erna Apriliani

    2011-01-01

    Full Text Available Kalman filter is an algorithm to estimate the state variable of dynamical stochastic system. The square root ensemble Kalman filter is an modification of Kalman filter. The square root ensemble Kalman filter is proposed to keep the computational stability and reduce the computational time. In this paper we study the efficiency of the reduced rank ensemble Kalman filter. We apply this algorithm to the non isothermal continue stirred tank reactor problem. We decompose the covariance of the ensemble estimation by using the singular value decomposition (the SVD, and then we reduced the rank of the diagonal matrix of those singular values. We make a simulation by using Matlab program. We took some the number of ensemble such as 100, 200 and 500. We compared the computational time and the accuracy between the square root ensemble Kalman filter and the ensemble Kalman filter. The reduced rank ensemble Kalman filter can’t be applied in this problem because the dimension of state variable is too less.

  19. Hybrid Kalman Filter: A New Approach for Aircraft Engine In-Flight Diagnostics

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2006-01-01

    In this paper, a uniquely structured Kalman filter is developed for its application to in-flight diagnostics of aircraft gas turbine engines. The Kalman filter is a hybrid of a nonlinear on-board engine model (OBEM) and piecewise linear models. The utilization of the nonlinear OBEM allows the reference health baseline of the in-flight diagnostic system to be updated to the degraded health condition of the engines through a relatively simple process. Through this health baseline update, the effectiveness of the in-flight diagnostic algorithm can be maintained as the health of the engine degrades over time. Another significant aspect of the hybrid Kalman filter methodology is its capability to take advantage of conventional linear and nonlinear Kalman filter approaches. Based on the hybrid Kalman filter, an in-flight fault detection system is developed, and its diagnostic capability is evaluated in a simulation environment. Through the evaluation, the suitability of the hybrid Kalman filter technique for aircraft engine in-flight diagnostics is demonstrated.

  20. Multi-scale Kalman filters algorithm for GPS common-view observation data based on correlation structure of discrete wavelet coefficients

    Institute of Scientific and Technical Information of China (English)

    OU Xiaojuan; ZHOU Wei

    2007-01-01

    Global positioning system (GPS)common-view observation data were processed by using the multi-scale Kalman algorithm based on a correlative structure of the discrete wavelet coefficients.Suppose that the GPS common-view observation data has the 1/f fractal characteristic,the algorithm of wavelet transform was used to estimate the Hurst parameter H of GPS clock difference data.When 0<H<1,the 1/f fractal characteristic of the GPS clock difference data iS a Gaussian zero-mean and non-stationary stochastic process.Thus,the discrete wavelet coefficients can be discussed in the process of estimating multi-scale Kalman coefficients.Furthermore,the discrete clock difierence can be estimated.The single-channel and multi-channel common-view observation data were processed respectively.Comparisons were made between the results obtained and the Circular T data.Simulation results show that the algorithm discussed in this paper is both feasible and effective.

  1. Tracking and convergence of multi-channel kalman filters for active noise control

    NARCIS (Netherlands)

    Berkhoff, A.; Ophem, S. van

    2013-01-01

    The feed-forward broadband active noise control problem can be formulated as a state estimation problem to achieve a faster rate of convergence than the filtered reference least mean squares algorithm and possibly also a better tracking performance. A multiple input/multiple output Kalman algorithm

  2. Tracking and convergence of multi-channel Kalman filters for active noise control

    NARCIS (Netherlands)

    Berkhoff, A.P.; Ophem, S. van

    2013-01-01

    The feed-forward broadband active noise control problem can be formulated as a state estimation problem to achieve a faster rate of convergence than the filtered reference least mean squares algorithm and possibly also a better tracking performance. A multiple input/multiple output Kalman algorithm

  3. Software alignment of the BESⅢ main drift chamber using the Kalman Filter method

    Institute of Scientific and Technical Information of China (English)

    WANG Ji-Ke; MAO Ze-Pu; BIAN Jian-Ming; CAO Guo-Fu; CAO Xue-Xiang; CHEN Shen-Jian; DENG Zi-Yan; FU Cheng-Dong; GAO Yuan-Ning; HE Kang-Lin; HE Miao; HUA Chun-Fei; HUANG Bin; HUANG Xing-Tao; JI Xiao-Bin; LI Fei; LI Hai-Bo; LI Wei-Dong; LIANG Yu-Tie; LIU Chun-Xiu; LIU Huai-Min; LIU Suo; LIU Ying-Jie; MA Qiu-Mei; MA Xiang; MAO Ya-Jun; MO Xiao-Hu; PAN Ming-Hua; PANG Cai-Ying; PING Rong-Gang; QIN Ya-Hong; QIU Jin-Fa; SUN Sheng-Sen; SUN Yong-Zhao; WANG Liang-Liang; WEN Shuo-Pin; WU Ling-Hui; XIE Yu-Guang; XU Min; YAN Liang; YOU Zheng-Yun; YUAN Chang-Zheng; YUAN Ye; ZHANG Bing-Yun; ZHANG Chang-Chun; ZHANG Jian-Yong; ZHANG Xue-Yao; ZHANG Yao; ZHENG Yang-Heng; ZHU Ke-Jun; ZHU Yong-Sheng; ZHU Zhi-Li; ZOU Jia-Heng

    2009-01-01

    Software alignment is quite important for a tracking detector to reach its ultimate position accuracy and momentum resolution. We developed a new alignment algorithm for the BESⅢ Main Drift Chamber using the Kalman Filter method. Two different types of data which are helix tracks and straight tracks are used to test this algorithm, and the results show that the design and implementation is successful.

  4. Longitudinal Factor Score Estimation Using the Kalman Filter.

    Science.gov (United States)

    Oud, Johan H.; And Others

    1990-01-01

    How longitudinal factor score estimation--the estimation of the evolution of factor scores for individual examinees over time--can profit from the Kalman filter technique is described. The Kalman estimates change more cautiously over time, have lower estimation error variances, and reproduce the LISREL program latent state correlations more…

  5. Ensemble Kalman filtering with residual nudging

    Directory of Open Access Journals (Sweden)

    Xiaodong Luo

    2012-10-01

    Full Text Available Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF by (in effect adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise, the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/or enhance its stability against filter divergence, especially in the small ensemble scenario.

  6. Ensemble Kalman filtering with residual nudging

    KAUST Repository

    Luo, X.

    2012-10-03

    Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF) by (in effect) adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise, the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/or enhance its stability against filter divergence, especially in the small ensemble scenario.

  7. Unscented Kalman filtering in the additive noise case

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The unscented Kalman filter(UKF) has four implementations in the additive noise case,according to whether the state is augmented with noise vectors and whether a new set of sigma points is redrawn from the predicted state(which is so-called resampling) for the observation prediction.This paper concerns the differences of performances for those implementations,such as accuracy,adaptability,computational complexity,etc.The conditionally equivalent relationships between the augmented and non-augmented unscented transforms(UTs) are proved for several sampling strategies that are commonly used.Then,we find that the augmented and non-augmented UKFs have the same filter results with the additive measurement noise,but only have the same state predictions with the additive process noise.Resampling is not believed to be necessary in some researches.However,we find out that resampling can be helpful for an adaptive Kalman gain.This will improve the convergence and accuracy of the filter when the large scale state modeling bias or unknown maneuvers occur.Finally,some universal designing principles for a practical UKF are given as follows:1) for the additive observation noise case,it’s better to use the non-augmented UKF;2) for the additive process noise case,when the small state modeling bias or maneuvers are involved,the non-resampling algorithms with state whether augmented or not are candidates for filters;3) the resampling and non-augmented algorithm is the only choice while the large state modeling bias or maneuvers are latent.

  8. Concrete ensemble Kalman filters with rigorous catastrophic filter divergence.

    Science.gov (United States)

    Kelly, David; Majda, Andrew J; Tong, Xin T

    2015-08-25

    The ensemble Kalman filter and ensemble square root filters are data assimilation methods used to combine high-dimensional, nonlinear dynamical models with observed data. Ensemble methods are indispensable tools in science and engineering and have enjoyed great success in geophysical sciences, because they allow for computationally cheap low-ensemble-state approximation for extremely high-dimensional turbulent forecast models. From a theoretical perspective, the dynamical properties of these methods are poorly understood. One of the central mysteries is the numerical phenomenon known as catastrophic filter divergence, whereby ensemble-state estimates explode to machine infinity, despite the true state remaining in a bounded region. In this article we provide a breakthrough insight into the phenomenon, by introducing a simple and natural forecast model that transparently exhibits catastrophic filter divergence under all ensemble methods and a large set of initializations. For this model, catastrophic filter divergence is not an artifact of numerical instability, but rather a true dynamical property of the filter. The divergence is not only validated numerically but also proven rigorously. The model cleanly illustrates mechanisms that give rise to catastrophic divergence and confirms intuitive accounts of the phenomena given in past literature.

  9. Concrete ensemble Kalman filters with rigorous catastrophic filter divergence

    Science.gov (United States)

    Kelly, David; Majda, Andrew J.; Tong, Xin T.

    2015-01-01

    The ensemble Kalman filter and ensemble square root filters are data assimilation methods used to combine high-dimensional, nonlinear dynamical models with observed data. Ensemble methods are indispensable tools in science and engineering and have enjoyed great success in geophysical sciences, because they allow for computationally cheap low-ensemble-state approximation for extremely high-dimensional turbulent forecast models. From a theoretical perspective, the dynamical properties of these methods are poorly understood. One of the central mysteries is the numerical phenomenon known as catastrophic filter divergence, whereby ensemble-state estimates explode to machine infinity, despite the true state remaining in a bounded region. In this article we provide a breakthrough insight into the phenomenon, by introducing a simple and natural forecast model that transparently exhibits catastrophic filter divergence under all ensemble methods and a large set of initializations. For this model, catastrophic filter divergence is not an artifact of numerical instability, but rather a true dynamical property of the filter. The divergence is not only validated numerically but also proven rigorously. The model cleanly illustrates mechanisms that give rise to catastrophic divergence and confirms intuitive accounts of the phenomena given in past literature. PMID:26261335

  10. 基于无迹卡尔曼滤波的四旋翼无人飞行器姿态估计算法%Attitude Estimation Algorithm of Four Rotor Unmanned Aircraft Based on Unscented Kalman Filter

    Institute of Scientific and Technical Information of China (English)

    朱岩; 付巍

    2014-01-01

    In order to resolve the problem of attitude estimation for four rotor unmanned aircraft ,unscented kalman filter (UKF) attitude estimation algorithm was proposed .Firstly ,Euler method was employed to de-scribe the aircraft attitude solution model ,and based on the solution model ,the aircraft system state equation and observation equation were established .Secondly ,UKF was used to obtain the attitude angles .The theory analysis and simulation results show that ,compared with the spacecraft attitude angle information obtained by extended Kalman filter algorithm ,although calculation time of UKF is slightly longer than that of extended kalman filter (EKF) ,the calculation accuracy and convergence rate of UKF is significantly higher than that of EK F .%针对四旋翼无人飞行器姿态测量问题,提了一种基于无迹卡尔曼滤波的飞行器姿态估计算法。采用欧拉角法描述了飞行器的姿态解算模型,并在此基础上建立了飞行器的系统状态方程和观测方程;采用无迹卡尔曼滤波算法实现飞行器姿态角的解算。理论分析和仿真表明:与采用扩展卡尔曼滤波算法获得的飞行器姿态角信息相比较,虽然无迹卡尔曼滤波算法的计算时间稍有增加,但无迹卡尔曼滤波算法的计算精度和收敛速度比扩展卡尔曼滤波算法有显著的提高。

  11. Identification of observer/Kalman filter Markov parameters: Theory and experiments

    Science.gov (United States)

    Juang, Jer-Nan; Phan, Minh; Horta, Lucas G.; Longman, Richard W.

    1991-01-01

    An algorithm to compute Markov parameters of an observer or Kalman filter from experimental input and output data is discussed. The Markov parameters can then be used for identification of a state space representation, with associated Kalman gain or observer gain, for the purpose of controller design. The algorithm is a non-recursive matrix version of two recursive algorithms developed in previous works for different purposes. The relationship between these other algorithms is developed. The new matrix formulation here gives insight into the existence and uniqueness of solutions of certain equations and gives bounds on the proper choice of observer order. It is shown that if one uses data containing noise, and seeks the fastest possible deterministic observer, the deadbeat observer, one instead obtains the Kalman filter, which is the fastest possible observer in the stochastic environment. Results are demonstrated in numerical studies and in experiments on an ten-bay truss structure.

  12. Orchard navigation using derivative free Kalman filtering

    DEFF Research Database (Denmark)

    Hansen, Søren; Bayramoglu, Enis; Andersen, Jens Christian

    2011-01-01

    This paper describes the use of derivative free filters for mobile robot localization and navigation in an orchard. The localization algorithm fuses odometry and gyro measurements with line features representing the surrounding fruit trees of the orchard. The line features are created on basis of 2...

  13. 弹箭定姿系统的改进卡尔曼滤波算法研究%Study on an Improved Kalman Filtering Algorithm in Projectile Attitude Measurement System

    Institute of Scientific and Technical Information of China (English)

    李茜; 贾旭东; 马寅峰; 崔敏; 王高; 杨磊

    2011-01-01

    针对陀螺存在随时间累积漂移误差的特性造成卡尔曼滤波数据发散的问题,提出以陀螺与磁强计组合为测量手段,小波变换和卡尔曼滤波相结合的方法.该方法由卡尔曼滤波器获得较为准确的测量信息,对陀螺和磁强计的信息分别在尺度3下进行分解,比较每层的细节系数和近似部分的系数获得误差信号,结合BP神经网络建立误差模型.当测量数据发散时,选用模型的估计值修正卡尔曼滤波测量值.实验结果比传统卡尔曼滤波算法精度提高了2°,能有效提高姿态测量的精度.%To prevent data divergence of the gyro caused by drift error in Kalman filtering, a method was put forward which taking the gyro and magnetometer as measurement instruments by combining the Kalman filtering with wavelet transform. The relatively accurate measurement information was obtained through the Kalman filter, and the. Information of the gyro and the magnetometer was decomposed under the three dimensions. The error signal was obtained by comparing each level's coefficient in detail part and the approximate part, and the error model was built up based on BP neural network. When the data was diverging, the estimated value of the model was used to replace the measured value of the Kalman filter. The simulation result indicated that the measurement accuracy of the method is 2° higher than the traditional Kalman algorithm, and the attitude measurement precision can be improved effectively.

  14. 基于改进的扩展卡尔曼滤波在GPS测速中的应用%Application of improved extended Kalman filtering algorithm in velocity determination with GPS

    Institute of Scientific and Technical Information of China (English)

    冯雪丽; 颜伏伍; 胡杰

    2016-01-01

    Dynamic velocity determination had to meet the higher requirement from the vehicle electronic control system. The algorithm combining finite⁃difference algorithm with extended Kalman filtering algorithm has higher accuracy in GPS veloci⁃ty determination than GPS location difference velocity determination method,but there is a larger measuring error during the fast turning of car. The finite⁃difference operation is adopted to achieve the priori error covariance matrix and the posterior error cova⁃riance matrix of filtering to enhance the convergence of the filtering process,and then the finite⁃difference extended Kalman fil⁃tering algorithm is used to calculate car’s real⁃time location,so as to get the real⁃time speed of the vehicle. The experiment re⁃sults show that the improved algorithm has higher measuring accuracy than that of the extended Kalman filtering velocity determi⁃nation algorithm.%汽车电子控制系统对动态测速提出较高的要求,使用位差法和扩展卡尔曼滤波融合算法用于GPS测速比GPS位置差分测速方法具有更高的精度,但在汽车快速转弯时误差较大。为此通过有限差分运算获取滤波验前、验后误差协方差矩阵,增强滤波过程的收敛性,再以有限差分扩展卡尔曼滤波算法对汽车实时位置进行运算以获取实时车速。实验结果表明,相比扩展卡尔曼滤波GPS测速方法,该文算法具有更高的测速精度。

  15. Modified Extended Kalman Filtering for Tracking with Insufficient and Intermittent Observations

    Directory of Open Access Journals (Sweden)

    Pengpeng Chen

    2015-01-01

    Full Text Available This paper is concerned with the Kalman filtering problem for tracking a single target on the fixed-topology wireless sensor networks (WSNs. Both the insufficient anchor coverage and the packet dropouts have been taken into consideration in the filter design. The resulting tracking system is modeled as a multichannel nonlinear system with multiplicative noise. Noting that the channels may be correlated with each other, we use a general matrix to express the multiplicative noise. Then, a modified extended Kalman filtering algorithm is presented based on the obtained model to achieve high tracking accuracy. In particular, we evaluate the effect of various parameters on the tracking performance through simulation studies.

  16. Attitude Estimation Based on the Spherical Simplex Transformation Modified Unscented Kalman Filter

    Directory of Open Access Journals (Sweden)

    Jianwei Zhao

    2014-01-01

    Full Text Available An antenna attitude estimation algorithm is proposed to improve the antenna pointing accuracy for the satellite communication on-the-move. The extrapolated angular acceleration is adopted to improve the performance of the time response. The states of the system are modified according to the modification rules. The spherical simplex transformation unscented Kalman filter is used to improve the precision of the estimated attitude and decrease the calculation of the unscented Kalman filter. The experiment results show that the proposed algorithm can improve the instantaneity of the estimated attitude and the precision of the antenna pointing, which meets the requirement of the antenna pointing.

  17. Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets.

    Science.gov (United States)

    Pérez-Ortiz, Juan Antonio; Gers, Felix A; Eck, Douglas; Schmidhuber, Jürgen

    2003-03-01

    The long short-term memory (LSTM) network trained by gradient descent solves difficult problems which traditional recurrent neural networks in general cannot. We have recently observed that the decoupled extended Kalman filter training algorithm allows for even better performance, reducing significantly the number of training steps when compared to the original gradient descent training algorithm. In this paper we present a set of experiments which are unsolvable by classical recurrent networks but which are solved elegantly and robustly and quickly by LSTM combined with Kalman filters.

  18. Kalman filter data assimilation: targeting observations and parameter estimation.

    Science.gov (United States)

    Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex

    2014-06-01

    This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.

  19. Simulation Research of SLAM Algorithm Based on Iterated Unscented Kalman Filter%基于迭代无迹卡尔曼滤波的SLAM算法仿真研究

    Institute of Scientific and Technical Information of China (English)

    陈晨; 程荫杭

    2012-01-01

    The SLAM algorithm based on iterated unscented Kalman filter was analyzed through simulation. Simulation results indicate that, comparing with the SLAM algorithm based on unscented Kalman filter, the iterated one could not improve the SLAM accuracy in some circumstances. To deal with this problem, the condition of using iteration in SLAM was discussed frst; the damped Gauss-Newton iteration was adopted to replace the traditional full Gauss-Newton iteration during the observation update process of SLAM algorithm. A new improved SLAM algorithm based on interated unscented Kanlman filter was proposed. The simulation experiments validate the proposed iteration condition. The experiment results show that the proposed algorithm can further improve the SLAM accuracy.%对迭代无迹卡尔曼滤波算法在SLAM问题中的应用进行仿真研究。通过仿真分析发现,与一般的无迹卡尔曼滤波算法相比,迭代的算法有时无法提高SLAM的精度,继而探讨了SLAM问题中选择采用迭代算法的条件;同时针对迭代算法的观测更新阶段,用阻尼的高斯-牛顿迭代方法改进完全高斯-牛顿迭代方法,从而提出一种改进的基于迭代无迹卡尔曼滤波的SLAM算法。仿真实验对提出的迭代条件进行了验证,仿真结果表明提出的SLAM算法与无迹卡尔曼滤波算法相比,可以进一步提高SLAM问题的估计精度。

  20. A Hierarchical Bayes Ensemble Kalman Filter

    Science.gov (United States)

    Tsyrulnikov, Michael; Rakitko, Alexander

    2017-01-01

    A new ensemble filter that allows for the uncertainty in the prior distribution is proposed and tested. The filter relies on the conditional Gaussian distribution of the state given the model-error and predictability-error covariance matrices. The latter are treated as random matrices and updated in a hierarchical Bayes scheme along with the state. The (hyper)prior distribution of the covariance matrices is assumed to be inverse Wishart. The new Hierarchical Bayes Ensemble Filter (HBEF) assimilates ensemble members as generalized observations and allows ordinary observations to influence the covariances. The actual probability distribution of the ensemble members is allowed to be different from the true one. An approximation that leads to a practicable analysis algorithm is proposed. The new filter is studied in numerical experiments with a doubly stochastic one-variable model of "truth". The model permits the assessment of the variance of the truth and the true filtering error variance at each time instance. The HBEF is shown to outperform the EnKF and the HEnKF by Myrseth and Omre (2010) in a wide range of filtering regimes in terms of performance of its primary and secondary filters.

  1. A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation

    KAUST Repository

    Altaf, Muhammad

    2014-08-01

    This study evaluates and compares the performances of several variants of the popular ensembleKalman filter for the assimilation of storm surge data with the advanced circulation (ADCIRC) model. Using meteorological data from Hurricane Ike to force the ADCIRC model on a domain including the Gulf ofMexico coastline, the authors implement and compare the standard stochastic ensembleKalman filter (EnKF) and three deterministic square root EnKFs: the singular evolutive interpolated Kalman (SEIK) filter, the ensemble transform Kalman filter (ETKF), and the ensemble adjustment Kalman filter (EAKF). Covariance inflation and localization are implemented in all of these filters. The results from twin experiments suggest that the square root ensemble filters could lead to very comparable performances with appropriate tuning of inflation and localization, suggesting that practical implementation details are at least as important as the choice of the square root ensemble filter itself. These filters also perform reasonably well with a relatively small ensemble size, whereas the stochastic EnKF requires larger ensemble sizes to provide similar accuracy for forecasts of storm surge.

  2. Attitude Determination for MAVs Using a Kalman Filter

    Institute of Scientific and Technical Information of China (English)

    LIU Cheng; ZHOU Zhaoying; FU Xu

    2008-01-01

    This paper presents a Kalman filter to effectively and economically determine the Euler angles for micro aerial vehicles(MAVs),whose size and payload are severely limited.The filter uses data from a series of micro-electro mechanical system sensors to determine the selected 3 vanables of the direction cosine matrix and the bias of the rata gyro sensors as state elements in a dynamic model,with the gravitational acceleration to build a measurement model.For high speed maneuvers,rigid motion equations are used to correct the measurements of the gravitational acceleration.The filter is designed to automatically tune its gain based on the dynamic system state.Simulations indicate that the Euler angles can be determined with standard deviations less than 3.The algorithm was successfully implemented in a miniature attitude measurement system suitable for MAVs.Aerobatic flights show that the attitude determination algorithm works effectively.The attitude determination algorithm is effective and economical,and can also be applied to bionic rebofishs and land vehicles,whose size and payload are also greatly limited.

  3. An Adaptive Kalman Filter Excisor for Suppressing Narrowband Interference

    Science.gov (United States)

    1993-11-01

    interferences in- connues. Le filtre de Kalman doit alors "apprendre" ý ajuster un de ses param~tres pour effectuer le meilleur traitement. L’erreur est...4"L l B"• -- -- - - -.- ,_, . An~. A)7cQ 0 -QGOP II liii 111111 IIa( Naional 06fenso I ’ I Deence nitonals I "It AN ADAPTIVE KALMAN FILTER EXCISOR...Ottawa 0 A o~ oO Best Available COpy 4INational Defense Defence nationals AN ADAPTIVE KALMAN FILTER EXCISOR FOR SUPPRESSING NARROWBAND INTERFERENCE by

  4. Cuff-less blood pressure measurement using pulse arrival time and a Kalman filter

    Science.gov (United States)

    Zhang, Qiang; Chen, Xianxiang; Fang, Zhen; Xue, Yongjiao; Zhan, Qingyuan; Yang, Ting; Xia, Shanhong

    2017-02-01

    The present study designs an algorithm to increase the accuracy of continuous blood pressure (BP) estimation. Pulse arrival time (PAT) has been widely used for continuous BP estimation. However, because of motion artifact and physiological activities, PAT-based methods are often troubled with low BP estimation accuracy. This paper used a signal quality modified Kalman filter to track blood pressure changes. A Kalman filter guarantees that BP estimation value is optimal in the sense of minimizing the mean square error. We propose a joint signal quality indice to adjust the measurement noise covariance, pushing the Kalman filter to weigh more heavily on measurements from cleaner data. Twenty 2 h physiological data segments selected from the MIMIC II database were used to evaluate the performance. Compared with straightforward use of the PAT-based linear regression model, the proposed model achieved higher measurement accuracy. Due to low computation complexity, the proposed algorithm can be easily transplanted into wearable sensor devices.

  5. Kalman filtering for neural prediction of response spectra from mining tremors

    Energy Technology Data Exchange (ETDEWEB)

    Krok, A.; Waszczyszyn, Z. [Cracow University of Technology, Krakow (Poland)

    2007-08-15

    Acceleration response spectra (ARS) for mining tremors in the Upper Silesian Coalfield, Poland are generated using neural networks trained by means of Kalman filtering. The target ARS were computed on the base of measured accelerograms. It was proved that the standard feed-forward, layered neural network, trained by the DEFK (decoupled extended Kalman filter) algorithm is numerically much less efficient than the standard recurrent NN learnt by Recurrent DEKF, cf. (Haykin S, (editor). Kalman filtering and neural networks. New York: John Wiley & Sons; 2001). It is also shown that the studied KF algorithms are better than the traditional Resilient-Propagation learning method. The improvement of the training process and neural prediction due to introduction of an autoregressive input is also discussed in the paper.

  6. 基于掩膜的加权卡尔曼滤波相位解缠算法%Weighted Kalman filter phase unwrapping algorithm based on mask and its analysis

    Institute of Scientific and Technical Information of China (English)

    闫满

    2013-01-01

    The Kalman filter transforms the phase unwrapping problem into the state estimate issue to deal with phase unwrapping and noise eliminating at the same time. But the original radar signal and post-processing producing a lot of errors and others can cause phase discontinuity and local error propagation, unwrapped result is not accurate. Therefore, weighted Kalman filter phase unwrapping algorithm based on mask is proposed. Through the low-quality region in wrapped phase is masked, Kalman filter is implemented in the high-quality region after masked. When the high-quality region is unwrapped correctly, weighted Kalman filter is implemented in the masked off low-quality region, then a reliable unwrapping result is obtained. Using simulated data and InSAR data of ALOS satellite in Shandong Yanzhou mining area, it is verified that the proposed algorithm is effective and reliable.%  卡尔曼滤波将相位解缠转化为状态估计问题,同时实现相位解缠与噪声消除。由于原始雷达信号以及后处理过程中产生的诸多误差,造成相位数据不连续产生局部误差传递,使得解缠结果不准确。提出一种基于掩膜的加权卡尔曼滤波相位解缠算法。该算法通过对包缠数据中的低质量区域进行掩膜处理,对掩膜后的高质量区域进行卡尔曼滤波相位解缠,再对掩膜区域实施加权卡尔曼滤波相位解缠,得到了较为可靠的相位解缠结果。采用仿真数据和ALOS卫星的山东兖矿地区干涉SAR数据进行实验,验证了算法的有效性和可靠性。

  7. Alternatives to an extended Kalman Filter for target image tracking

    Science.gov (United States)

    Leuthauser, P. R.

    1981-12-01

    Four alternative filters are compared to an extended Kalman filter (EKF) algorithm for tracking a distributed (elliptical) source target in a closed loop tracking problem, using outputs from a forward looking (FLIR) sensor as measurements. These were (1) an EKF with (second order) bias correction term, (2) a constant gain EKF, (3) a constant gain EKF with bias correction term, and (4) a statistically linearized filter. Estimates are made of both actual target motion and of apparent motion due to atmospheric jitter. These alternative designs are considered specifically to address some of the significant biases exhibited by an EKF due to initial acquisition difficulties, unmodelled maneuvering by the target, low signal-to-noise ratio, and real world conditions varying significantly from those assumed in the filter design (robustness). Filter performance was determined with a Monte Carlo study under both ideal and non ideal conditions for tracking targets on a constant velocity cross range path, and during constant acceleration turns of 5G, 10G, and 20G.

  8. Kinematic landslide monitoring with Kalman filtering

    Directory of Open Access Journals (Sweden)

    M. Acar

    2008-03-01

    Full Text Available Landslides are serious geologic disasters that threat human life and property in every country. In addition, landslides are one of the most important natural phenomena, which directly or indirectly affect countries' economy. Turkey is also the country that is under the threat of landslides. Landslides frequently occur in all of the Black Sea region as well as in many parts of Marmara, East Anatolia, and Mediterranean regions. Since these landslides resulted in destruction, they are ranked as the second important natural phenomenon that comes after earthquake in Turkey. In recent years several landslides happened after heavy rains and the resulting floods. This makes the landslide monitoring and mitigation techniques an important study subject for the related professional disciplines in Turkey. The investigations on surface deformations are conducted to define the boundaries of the landslide, size, level of activity and direction(s of the movement, and to determine individual moving blocks of the main slide.

    This study focuses on the use of a kinematic deformation analysis based on Kalman Filtering at a landslide area near Istanbul. Kinematic deformation analysis has been applied in a landslide area, which is located to the north of Istanbul city. Positional data were collected using GPS technique. As part of the study, conventional static deformation analysis methodology has also been applied on the same data. The results and comparisons are discussed in this paper.

  9. Multivariate localization methods for ensemble Kalman filtering

    KAUST Repository

    Roh, S.

    2015-05-08

    In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

  10. Multivariate localization methods for ensemble Kalman filtering

    KAUST Repository

    Roh, S.

    2015-12-03

    In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

  11. Multivariate localization methods for ensemble Kalman filtering

    Directory of Open Access Journals (Sweden)

    S. Roh

    2015-05-01

    Full Text Available In ensemble Kalman filtering (EnKF, the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

  12. Ensemble Kalman filtering with residual nudging

    CERN Document Server

    Luo, Xiaodong; 10.3402/tellusa.v64i0.17130

    2012-01-01

    Covariance inflation and localization are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF) by (in effect) adjusting the sample covariances of the estimates in the state space. In this work an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/o...

  13. Multivariate localization methods for ensemble Kalman filtering

    Science.gov (United States)

    Roh, S.; Jun, M.; Szunyogh, I.; Genton, M. G.

    2015-12-01

    In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

  14. Hierarchical Bayes Ensemble Kalman Filter for geophysical data assimilation

    Science.gov (United States)

    Tsyrulnikov, Michael; Rakitko, Alexander

    2016-04-01

    step, the new filter named Hierarchical Bayes Ensemble Kalman Filter (HBEF) employs a full-fledged secondary filter that cycles and updates the covariance matrices P and Q. Approximations that lead to practicable analysis algorithms are proposed and tested. The HBEF is studied in numerical experiments with a one-variable model of "truth" and found significantly superior to EnKF in a wide range of filtering regimes. Results of numerical experiments with multi-variable models of truth are also presented.

  15. Motion estimation using point cluster method and Kalman filter.

    Science.gov (United States)

    Senesh, M; Wolf, A

    2009-05-01

    The most frequently used method in a three dimensional human gait analysis involves placing markers on the skin of the analyzed segment. This introduces a significant artifact, which strongly influences the bone position and orientation and joint kinematic estimates. In this study, we tested and evaluated the effect of adding a Kalman filter procedure to the previously reported point cluster technique (PCT) in the estimation of a rigid body motion. We demonstrated the procedures by motion analysis of a compound planar pendulum from indirect opto-electronic measurements of markers attached to an elastic appendage that is restrained to slide along the rigid body long axis. The elastic frequency is close to the pendulum frequency, as in the biomechanical problem, where the soft tissue frequency content is similar to the actual movement of the bones. Comparison of the real pendulum angle to that obtained by several estimation procedures--PCT, Kalman filter followed by PCT, and low pass filter followed by PCT--enables evaluation of the accuracy of the procedures. When comparing the maximal amplitude, no effect was noted by adding the Kalman filter; however, a closer look at the signal revealed that the estimated angle based only on the PCT method was very noisy with fluctuation, while the estimated angle based on the Kalman filter followed by the PCT was a smooth signal. It was also noted that the instantaneous frequencies obtained from the estimated angle based on the PCT method is more dispersed than those obtained from the estimated angle based on Kalman filter followed by the PCT method. Addition of a Kalman filter to the PCT method in the estimation procedure of rigid body motion results in a smoother signal that better represents the real motion, with less signal distortion than when using a digital low pass filter. Furthermore, it can be concluded that adding a Kalman filter to the PCT procedure substantially reduces the dispersion of the maximal and minimal

  16. Kalman-Takens filtering in the presence of dynamical noise

    CERN Document Server

    Hamilton, Franz; Sauer, Timothy

    2016-01-01

    The use of data assimilation for the merging of observed data with dynamical models is becoming standard in modern physics. If a parametric model is known, methods such as Kalman filtering have been developed for this purpose. If no model is known, a hybrid Kalman-Takens method has been recently introduced, in order to exploit the advantages of optimal filtering in a nonparametric setting. This procedure replaces the parametric model with dynamics reconstructed from delay coordinates, while using the Kalman update formulation to assimilate new observations. We find that this hybrid approach results in comparable efficiency to parametric methods in identifying underlying dynamics, even in the presence of dynamical noise. By combining the Kalman-Takens method with an adaptive filtering procedure we are able to estimate the statistics of the observational and dynamical noise. This solves a long standing problem of separating dynamical and observational noise in time series data, which is especially challenging w...

  17. Impulse control in Kalman-like filtering problems

    Directory of Open Access Journals (Sweden)

    Michael V. Basin

    1998-01-01

    Full Text Available This paper develops the impulse control approach to the observation process in Kalman-like filtering problems, which is based on impulsive modeling of the transition matrix in an observation equation. The impulse control generates the jumps of the estimate variance from its current position down to zero and, as a result, enables us to obtain the filtering equations for the Kalman estimate with zero variance for all post-jump time moments. The filtering equations for the estimates with zero variances are obtained in the conventional linear filtering problem and in the case of scalar nonlinear state and nonlinear observation equations.

  18. Algorithm Design and Comparison of Kalman and FIR Filter Methods for Image Stabilization of Robot Vision%机器人视觉Kalman和FIR滤波稳像算法设计与比较

    Institute of Scientific and Technical Information of China (English)

    金英连; 王斌锐; 徐崟

    2013-01-01

    稳像是提高基于视觉的移动机器人作业精度的关键.论文建立了完整的稳像算法流程,包含图像运动学模型、KLT特征提取、SAD特征匹配和滤波算法;设计了运动参数的Kalman和FIR滤波算法;并利用MATLAB实现了运动参数的Kalman和FIR滤波器;仿真验证和对比分析了Kalman和FIR滤波器对运动参数的去抖效果.结果表明,机器人视觉稳像中,Kalman滤波效果优于FIR滤波.用VC++和OpenCV编程实现了基于Kalman滤波的机器人视觉稳像软件,在双机器人移动平台上开展了实验,稳像计算时间小于视频采样时间,系统满足机器人对接作业实时性和精度要求.%Image stabilization is the key for accurate docking operations of robots with vision. The whole algorithm of image stabilization is established, including images kinematics model, KLT feature pixels detecting, SAD feature pixels matching and filters. Kalman and FIR filters are designed for smoothing images motion parameters and built in MATLAB. Simulation of filter of motion un-intended parameters is implemented to indicate removing jitter effect. Kalman filter is compared with FIR filter. Comparison curves and tables are given , which demonstrate that Kalman filter is better than FIR in robot vision image stabilization process. Based on VC++ and OpenCV, image stabilization software is programmed, and experiments are completed on double moving robots docking operation platform. The algorithm running time is less than the sampling period, and the precision and real-time demands are contented.

  19. Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.

    Science.gov (United States)

    Muruganantham, Arrchana; Tan, Kay Chen; Vadakkepat, Prahlad

    2016-12-01

    Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.

  20. Reduced-Order Kalman Filtering for Processing Relative Measurements

    Science.gov (United States)

    Bayard, David S.

    2008-01-01

    A study in Kalman-filter theory has led to a method of processing relative measurements to estimate the current state of a physical system, using less computation than has previously been thought necessary. As used here, relative measurements signifies measurements that yield information on the relationship between a later and an earlier state of the system. An important example of relative measurements arises in computer vision: Information on relative motion is extracted by comparing images taken at two different times. Relative measurements do not directly fit into standard Kalman filter theory, in which measurements are restricted to those indicative of only the current state of the system. One approach heretofore followed in utilizing relative measurements in Kalman filtering, denoted state augmentation, involves augmenting the state of the system at the earlier of two time instants and then propagating the state to the later time instant.While state augmentation is conceptually simple, it can also be computationally prohibitive because it doubles the number of states in the Kalman filter. When processing a relative measurement, if one were to follow the state-augmentation approach as practiced heretofore, one would find it necessary to propagate the full augmented state Kalman filter from the earlier time to the later time and then select out the reduced-order components. The main result of the study reported here is proof of a property called reduced-order equivalence (ROE). The main consequence of ROE is that it is not necessary to augment with the full state, but, rather, only the portion of the state that is explicitly used in the partial relative measurement. In other words, it suffices to select the reduced-order components first and then propagate the partial augmented state Kalman filter from the earlier time to the later time; the amount of computation needed to do this can be substantially less than that needed for propagating the full augmented

  1. Efficient decoding with steady-state Kalman filter in neural interface systems.

    Science.gov (United States)

    Malik, Wasim Q; Truccolo, Wilson; Brown, Emery N; Hochberg, Leigh R

    2011-02-01

    The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5±0.5 s (mean ±s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25±3 single units by a factor of 7.0±0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.

  2. Switching Kalman filter for failure prognostic

    Science.gov (United States)

    Lim, Chi Keong Reuben; Mba, David

    2015-02-01

    The use of condition monitoring (CM) data to predict remaining useful life have been growing with increasing use of health and usage monitoring systems on aircraft. There are many data-driven methodologies available for the prediction and popular ones include artificial intelligence and statistical based approach. The drawback of such approaches is that they require a lot of failure data for training which can be scarce in practice. In lieu of this, methods using state-space and regression-based models that extract information from the data history itself have been explored. However, such methods have their own limitations as they utilize a single time-invariant model which does not represent changing degradation path well. This causes most degradation modeling studies to focus only on segments of their CM data that behaves close to the assumed model. In this paper, a state-space based method; the Switching Kalman Filter (SKF), is adopted for model estimation and life prediction. The SKF approach however, uses multiple models from which the most probable model is inferred from the CM data using Bayesian estimation before it is applied for prediction. At the same time, the inference of the degradation model itself can provide maintainers with more information for their planning. This SKF approach is demonstrated with a case study on gearbox bearings that were found defective from the Republic of Singapore Air Force AH64D helicopter. The use of in-service CM data allows the approach to be applied in a practical scenario and results showed that the developed SKF approach is a promising tool to support maintenance decision-making.

  3. Kalman Filter for Calibrating a Telescope Focal Plane

    Science.gov (United States)

    Kang, Bryan; Bayard, David

    2006-01-01

    The instrument-pointing frame (IPF) Kalman filter, and an algorithm that implements this filter, have been devised for calibrating the focal plane of a telescope. As used here, calibration signifies, more specifically, a combination of measurements and calculations directed toward ensuring accuracy in aiming the telescope and determining the locations of objects imaged in various arrays of photodetectors in instruments located on the focal plane. The IPF Kalman filter was originally intended for application to a spaceborne infrared astronomical telescope, but can also be applied to other spaceborne and ground-based telescopes. In the traditional approach to calibration of a telescope, (1) one team of experts concentrates on estimating parameters (e.g., pointing alignments and gyroscope drifts) that are classified as being of primarily an engineering nature, (2) another team of experts concentrates on estimating calibration parameters (e.g., plate scales and optical distortions) that are classified as being primarily of a scientific nature, and (3) the two teams repeatedly exchange data in an iterative process in which each team refines its estimates with the help of the data provided by the other team. This iterative process is inefficient and uneconomical because it is time-consuming and entails the maintenance of two survey teams and the development of computer programs specific to the requirements of each team. Moreover, theoretical analysis reveals that the engineering/ science iterative approach is not optimal in that it does not yield the best estimates of focal-plane parameters and, depending on the application, may not even enable convergence toward a set of estimates.

  4. Strong tracking adaptive Kalman filters for underwater vehicle dead reckoning

    Institute of Scientific and Technical Information of China (English)

    XIAO Kun; FANG Shao-ji; PANG Yong-jie

    2007-01-01

    To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.

  5. Extended Kalman Filter Based Neural Networks Controller For Hot Strip Rolling mill

    Science.gov (United States)

    Moussaoui, A. K.; Abbassi, H. A.; Bouazza, S.

    2008-06-01

    The present paper deals with the application of an Extended Kalman filter based adaptive Neural-Network control scheme to improve the performance of a hot strip rolling mill. The suggested Neural Network model was implemented using Bayesian Evidence based training algorithm. The control input was estimated iteratively by an on-line extended Kalman filter updating scheme basing on the inversion of the learned neural networks model. The performance of the controller is evaluated using an accurate model estimated from real rolling mill input/output data, and the usefulness of the suggested method is proved.

  6. Modeling of HVDC in Dynamic State Estimation Using Unscented Kalman Filter Method

    DEFF Research Database (Denmark)

    Khazraj, Hesam; Silva, Filipe Miguel Faria da; Bak, Claus Leth

    2016-01-01

    HVDC transmission is an integral part of various power system networks. This article presents an Unscented Kalman Filter dynamic state estimator algorithm that considers the presence of HVDC links. The AC - DC power flow analysis, which is implemented as power flow solver for Dynamic State...

  7. Alternate approach for terrain-aided navigation using parallel extended Kalman filters

    Energy Technology Data Exchange (ETDEWEB)

    Sheives, T.C.; Andreas, R.D.

    1979-12-01

    A new approach for applying SITAN (Sandia Inertial Terrain Aided Navigation) to applications involving large initial position errors is described and analyzed. The approach uses parallel Kalman filters in combination with a selection algorithm to estimate the errors in the reference navigation system. Monte Carlo simulation and covariance analysis results are presented which demonstrate the feasibility and practicality of the approach.

  8. Nonlinear Kalman Filtering for acoustic emission source localization in anisotropic panels.

    Science.gov (United States)

    Dehghan Niri, E; Farhidzadeh, A; Salamone, S

    2014-02-01

    Nonlinear Kalman Filtering is an established field in applied probability and control systems, which plays an important role in many practical applications from target tracking to weather and climate prediction. However, its application for acoustic emission (AE) source localization has been very limited. In this paper, two well-known nonlinear Kalman Filtering algorithms are presented to estimate the location of AE sources in anisotropic panels: the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). These algorithms are applied to two cases: velocity profile known (CASE I) and velocity profile unknown (CASE II). The algorithms are compared with a more traditional nonlinear least squares method. Experimental tests are carried out on a carbon-fiber reinforced polymer (CFRP) composite panel instrumented with a sparse array of piezoelectric transducers to validate the proposed approaches. AE sources are simulated using an instrumented miniature impulse hammer. In order to evaluate the performance of the algorithms, two metrics are used: (1) accuracy of the AE source localization and (2) computational cost. Furthermore, it is shown that both EKF and UKF can provide a confidence interval of the estimated AE source location and can account for uncertainty in time of flight measurements.

  9. State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

    Directory of Open Access Journals (Sweden)

    Bizhong Xia

    2015-06-01

    Full Text Available Accurate state of charge (SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST and New European Driving Cycle (NEDC are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF and cubature Kalman filter (CKF algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms.

  10. Kalman filter for speech enhancement in cocktail party scenarios using a codebook-based approach

    DEFF Research Database (Denmark)

    Kavalekalam, Mathew Shaji; Christensen, Mads Græsbøll; Gran, Fredrik;

    2016-01-01

    Enhancement of speech in non-stationary background noise is a challenging task, and conventional single channel speech enhancement algorithms have not been able to improve the speech intelligibility in such scenarios. The work proposed in this paper investigates a single channel Kalman filter based...

  11. Signal Conditioning for the Kalman Filter: Application to Satellite Attitude Estimation with Magnetometer and Sun Sensors.

    Science.gov (United States)

    Esteban, Segundo; Girón-Sierra, Jose M; Polo, Óscar R; Angulo, Manuel

    2016-10-31

    Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework.

  12. Signal Conditioning for the Kalman Filter: Application to Satellite Attitude Estimation with Magnetometer and Sun Sensors

    Directory of Open Access Journals (Sweden)

    Segundo Esteban

    2016-10-01

    Full Text Available Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework.

  13. Applications of Kalman filters based on non-linear functions to numerical weather predictions

    Directory of Open Access Journals (Sweden)

    G. Galanis

    2006-10-01

    Full Text Available This paper investigates the use of non-linear functions in classical Kalman filter algorithms on the improvement of regional weather forecasts. The main aim is the implementation of non linear polynomial mappings in a usual linear Kalman filter in order to simulate better non linear problems in numerical weather prediction. In addition, the optimal order of the polynomials applied for such a filter is identified. This work is based on observations and corresponding numerical weather predictions of two meteorological parameters characterized by essential differences in their evolution in time, namely, air temperature and wind speed. It is shown that in both cases, a polynomial of low order is adequate for eliminating any systematic error, while higher order functions lead to instabilities in the filtered results having, at the same time, trivial contribution to the sensitivity of the filter. It is further demonstrated that the filter is independent of the time period and the geographic location of application.

  14. Estimation of Kalman filter gain from output residuals

    Science.gov (United States)

    Juang, Jer-Nan; Chen, Chung-Wen; Phan, Minh

    1993-01-01

    This paper presents a procedure to estimate the Kalman filter gain from input-output measurement data with a given system model. The system model can be a finite element model or an experimental model from any identification method. The procedure consists of three basic steps. First, the stochastic portion related to the residuals of the response is computed. Second, the coefficients of a linear difference model for the stochastic portion are estimated by a least-squares solution that minimizes the filter residual. Third, the Kalman filter gain is computed from these model coefficients. Experimental results are presented to illustrate the usefulness of the developed procedure.

  15. On the equivalence of Kalman filtering and least-squares estimation

    Science.gov (United States)

    Mysen, E.

    2016-07-01

    The Kalman filter is derived directly from the least-squares estimator, and generalized to accommodate stochastic processes with time variable memory. To complete the link between least-squares estimation and Kalman filtering of first-order Markov processes, a recursive algorithm is presented for the computation of the off-diagonal elements of the a posteriori least-squares error covariance. As a result of the algebraic equivalence of the two estimators, both approaches can fully benefit from the advantages implied by their individual perspectives. In particular, it is shown how Kalman filter solutions can be integrated into the normal equation formalism that is used for intra- and inter-technique combination of space geodetic data.

  16. On the equivalence of Kalman filtering and least-squares estimation

    Science.gov (United States)

    Mysen, E.

    2017-01-01

    The Kalman filter is derived directly from the least-squares estimator, and generalized to accommodate stochastic processes with time variable memory. To complete the link between least-squares estimation and Kalman filtering of first-order Markov processes, a recursive algorithm is presented for the computation of the off-diagonal elements of the a posteriori least-squares error covariance. As a result of the algebraic equivalence of the two estimators, both approaches can fully benefit from the advantages implied by their individual perspectives. In particular, it is shown how Kalman filter solutions can be integrated into the normal equation formalism that is used for intra- and inter-technique combination of space geodetic data.

  17. Data assimilation in integrated hydrological modeling using ensemble Kalman filtering

    DEFF Research Database (Denmark)

    Rasmussen, Jørn; Madsen, H.; Jensen, Karsten Høgh

    2015-01-01

    Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members...

  18. Data assimilation in integrated hydrological modeling using ensemble Kalman filtering

    DEFF Research Database (Denmark)

    Rasmussen, Jørn; Madsen, H.; Jensen, Karsten Høgh;

    2015-01-01

    Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members...

  19. Bridging the ensemble Kalman filter and particle filters: the adaptive Gaussian mixture filter

    OpenAIRE

    Stordal, Andreas Størksen; Karlsen, Hans A.; Nævdal, Geir; Hans J. Skaug; Vallès, Brice

    2010-01-01

    The nonlinear filtering problem occurs in many scientific areas. Sequential Monte Carlo solutions with the correct asymptotic behavior such as particle filters exist, but they are computationally too expensive when working with high-dimensional systems. The ensemble Kalman filter (EnKF) is a more robust method that has shown promising results with a small sample size, but the samples are not guaranteed to come from the true posterior distribution. By approximating the model error with a Gauss...

  20. 基于多尺度贯序式卡尔曼滤波的运动声阵列跟踪算法%Tracking Algorithm of Motion Acoustic Array Based on Multi-scale Sequential Kalman Filter

    Institute of Scientific and Technical Information of China (English)

    刘亚雷; 颐晓辉

    2011-01-01

    为了研究运动声阵列对二维目标在复杂环境中的实时跟踪性问题,根据运动声阵列及二维目标的运动特点建立了状态方程与测量方程,并将其描述为块的形式.根据不同的状态块,利用小波变换把状态块分解到不同尺度上,分别在时域和频域上建立相应尺度上的状态与观测信息之间的关系;采取卡尔曼滤波器递推思想来实现运动声阵列的多尺度贯序式卡尔曼滤波算法,根据最小二乘误差估计理论推导了运动声阵列跟踪系统在球坐标系和直角坐标系下的误差,为提高系统跟踪精度奠定了理论基础,并为工程应用提供了实际方法.与传统的卡尔曼滤波算法相比,Matlab仿真结果表明了本文算法的有效性和优越性.%In order to study the motion acoustic array's real-time tracking to two-dimensional target in complex environment, the state equation and measurement equation based on the motion characteristics of dynamic acoustic array and two-dimensional target are established and converted into block form. Then, the state blocks are assigned onto different scales by wavelet transform. The relationship between the state and the measurement information in corresponding scale is established in time domain and frequency domain. After that, the multi-scale sequential Kalman filter algorithm is obtained based on the Kalman filter recursive theory, and the errors of motion acoustic array tracking system in spherical coordinates and rectangular coordinates are deduced by least square error estimation, which lays the theoretical foundation for improving the system tracking precision and provides a practical method for application. Compared with the traditional Kalman filter algorithm, the presented algorithm shows its validity and superiority in Matlab simulation.

  1. Adaptive Iterated Square-Root Cubature Kalman Filter and Its Application to SLAM of a Mobile Robot

    Directory of Open Access Journals (Sweden)

    Zuguo Chen

    2013-07-01

    Full Text Available For the mobile robot Simultaneous Localization and Mapping(SLAM,a new algorithm is proposed, and named Adaptive Iterated Square-Root Cubature Kalman Filter based SLAM algorithm(AISRCKF-SLAM. The main contribution of the algorithm is that the numerical integration method based on cubature rule is directly used to calculate the SLAM posterior probability density. To improve innovation covariance and cross-covariance, the latest measurements are iteratively used in the measurement updating. The algorithm can reduce linearization error and improve the accuracy of the SLAM algorithm. The algorithm also used adaptive iterating estimation restricted by the iterative sentencing guideline to adjust the proportion of the observation and dynamic model, to make the estimated square root of the error covariance more accurate and reasonable. In experiments, the proposed algorithm is compared with Extended Kalman Filter based SLAM algorithm (EKF-SLAM, Unscented Kalman Filter based SLAM algorithm (UKF-SLAM and Square-Root Cubature Kalman Filter based SLAM algorithm (SRCKF-SLAM. The results indicate that the proposed algorithm having with the higher accuracy of the state estimation is obtained to compare with the EKF-SLAM algorithm, the UKF-SLAM algorithm and the SRCKF-SLAM algorithm.

  2. Unscented Kalman Filter Applied to the Spacecraft Attitude Estimation with Euler Angles

    Directory of Open Access Journals (Sweden)

    Roberta Veloso Garcia

    2012-01-01

    Full Text Available The aim of this work is to test an algorithm to estimate, in real time, the attitude of an artificial satellite using real data supplied by attitude sensors that are on board of the CBERS-2 satellite (China Brazil Earth Resources Satellite. The real-time estimator used in this work for attitude determination is the Unscented Kalman Filter. This filter is a new alternative to the extended Kalman filter usually applied to the estimation and control problems of attitude and orbit. This algorithm is capable of carrying out estimation of the states of nonlinear systems, without the necessity of linearization of the nonlinear functions present in the model. This estimation is possible due to a transformation that generates a set of vectors that, suffering a nonlinear transformation, preserves the same mean and covariance of the random variables before the transformation. The performance will be evaluated and analyzed through the comparison between the Unscented Kalman filter and the extended Kalman filter results, by using real onboard data.

  3. Pico satellite attitude estimation via Robust Unscented Kalman Filter in the presence of measurement faults.

    Science.gov (United States)

    Soken, Halil Ersin; Hajiyev, Chingiz

    2010-07-01

    In the normal operation conditions of a pico satellite, a conventional Unscented Kalman Filter (UKF) gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, UKF gives inaccurate results and diverges by time. This study introduces Robust Unscented Kalman Filter (RUKF) algorithms with the filter gain correction for the case of measurement malfunctions. By the use of defined variables named as measurement noise scale factor, the faulty measurements are taken into consideration with a small weight, and the estimations are corrected without affecting the characteristics of the accurate ones. Two different RUKF algorithms, one with single scale factor and one with multiple scale factors, are proposed and applied for the attitude estimation process of a pico satellite. The results of these algorithms are compared for different types of measurement faults in different estimation scenarios and recommendations about their applications are given.

  4. Deconvolution Kalman filtering for force measurements of revolving wings

    Science.gov (United States)

    Vester, R.; Percin, M.; van Oudheusden, B.

    2016-09-01

    The applicability of a deconvolution Kalman filtering approach is assessed for the force measurements on a flat plate undergoing a revolving motion, as an alternative procedure to correct for test setup vibrations. The system identification process required for the correct implementation of the deconvolution Kalman filter is explained in detail. It is found that in the presence of a relatively complex forcing history, the DK filter is better suited to filter out structural test rig vibrations than conventional filtering techniques that are based on, for example, low-pass or moving-average filtering. The improvement is especially found in the characterization of the generated force peaks. Consequently, more reliable force data is obtained, which is vital to validate semi-empirical estimation models, but is also relevant to correlate identified flow phenomena to the force production.

  5. Nonlinear dynamical system identification using unscented Kalman filter

    Science.gov (United States)

    Rehman, M. Javvad ur; Dass, Sarat Chandra; Asirvadam, Vijanth Sagayan

    2016-11-01

    Kalman Filter is the most suitable choice for linear state space and Gaussian error distribution from decades. In general practical systems are not linear and Gaussian so these assumptions give inconsistent results. System Identification for nonlinear dynamical systems is a difficult task to perform. Usually, Extended Kalman Filter (EKF) is used to deal with non-linearity in which Jacobian method is used for linearizing the system dynamics, But it has been observed that in highly non-linear environment performance of EKF is poor. Unscented Kalman Filter (UKF) is proposed here as a better option because instead of analytical linearization of state space, UKF performs statistical linearization by using sigma point calculated from deterministic samples. Formation of the posterior distribution is based on the propagation of mean and covariance through sigma points.

  6. Detecting Power Voltage Dips using Tracking Filters - A Comparison against Kalman

    Directory of Open Access Journals (Sweden)

    STANCIU, I.-R.

    2012-11-01

    Full Text Available Due of its significant economical impact, Power-Quality (PQ analysis is an important domain today. Severe voltage distortions affect the consumers and disturb their activity. They may be caused by short circuits (in this case the voltage drops significantly or by varying loads (with a smaller drop. These two types are the PQ currently issues. Monitoring these phenomena (called dips or sags require powerful techniques. Digital Signal Processing (DSP algorithms are currently employed to fulfill this task. Discrete Wavelet Transforms, (and variants, Kalman filters, and S-Transform are currently proposed by researchers to detect voltage dips. This paper introduces and examines a new tool to detect voltage dips: the so-called tracking filters. Discovered and tested during the cold war, they can estimate a parameter of interest one-step-ahead based on the previously observed values. Two filters are implemented. Their performance is assessed by comparison against the Kalman filter?s results.

  7. The path prediction of cyclones with Kalman filters

    OpenAIRE

    Taskin, Dogan

    1990-01-01

    Approved for public release; distribution unlimited. The Kalman filter is used to provide estimates of the position and velocity of a storm based upon observation of the storm's longitude and latitude. Nonstationary noise is shown to degrade the performance of the filter and cause tracking divergence. Time varying values for the noise covariance matricies R and Q, and the addition of an external forcing function to the filter, effectively compensated for this tracking error. Results for th...

  8. Low-Rank Kalman Filtering in Subsurface Contaminant Transport Models

    KAUST Repository

    El Gharamti, Mohamad

    2010-12-01

    Understanding the geology and the hydrology of the subsurface is important to model the fluid flow and the behavior of the contaminant. It is essential to have an accurate knowledge of the movement of the contaminants in the porous media in order to track them and later extract them from the aquifer. A two-dimensional flow model is studied and then applied on a linear contaminant transport model in the same porous medium. Because of possible different sources of uncertainties, the deterministic model by itself cannot give exact estimations for the future contaminant state. Incorporating observations in the model can guide it to the true state. This is usually done using the Kalman filter (KF) when the system is linear and the extended Kalman filter (EKF) when the system is nonlinear. To overcome the high computational cost required by the KF, we use the singular evolutive Kalman filter (SEKF) and the singular evolutive extended Kalman filter (SEEKF) approximations of the KF operating with low-rank covariance matrices. The SEKF can be implemented on large dimensional contaminant problems while the usage of the KF is not possible. Experimental results show that with perfect and imperfect models, the low rank filters can provide as much accurate estimates as the full KF but at much less computational cost. Localization can help the filter analysis as long as there are enough neighborhood data to the point being analyzed. Estimating the permeabilities of the aquifer is successfully tackled using both the EKF and the SEEKF.

  9. A THEORETICAL STUDY ON SIMPLIFIED KALMAN FILTER IN DATA ASSIMILATION

    Institute of Scientific and Technical Information of China (English)

    Ma Zhai-pu; Huang Da-ji; Zhang Ben-zhao

    2003-01-01

    In this paper, we put forward a new method to reduce the calculation amount of the gain matrix of Kalman filter in data assimilation. We rewrite the vector describing the total state variables with two vectors whose dimensions are small and thus obtain the main parts and the trivial parts of the state variables. On the basis of the rewrittten formula, we not only develop a reduced Kalman filter scheme, but also obtain the transition equations about truncation errors, with which the validity of the main parts acting for the total state variables can be evaluated quantitatively. The error transition equations thus offer an indirect testimony to the rationality of the main parts.

  10. Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter

    Directory of Open Access Journals (Sweden)

    Hairong Chu

    2017-01-01

    Full Text Available In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF algorithm is proposed. First, the model of SINS transfer alignment is defined based on the “Velocity and Attitude” matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment.

  11. Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter.

    Science.gov (United States)

    Chu, Hairong; Sun, Tingting; Zhang, Baiqiang; Zhang, Hongwei; Chen, Yang

    2017-01-14

    In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed. First, the model of SINS transfer alignment is defined based on the "Velocity and Attitude" matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment.

  12. [Characteristic wavelength variable optimization of near-infrared spectroscopy based on Kalman filtering].

    Science.gov (United States)

    Wang, Li-Qi; Ge, Hui-Fang; Li, Gui-Bin; Yu, Dian-Yu; Hu, Li-Zhi; Jiang, Lian-Zhou

    2014-04-01

    Combining classical Kalman filter with NIR analysis technology, a new method of characteristic wavelength variable selection, namely Kalman filtering method, is presented. The principle of Kalman filter for selecting optimal wavelength variable was analyzed. The wavelength selection algorithm was designed and applied to NIR detection of soybean oil acid value. First, the PLS (partial leastsquares) models were established by using different absorption bands of oil. The 4 472-5 000 cm(-1) characteristic band of oil acid value, including 132 wavelengths, was selected preliminarily. Then the Kalman filter was used to select characteristic wavelengths further. The PLS calibration model was established using selected 22 characteristic wavelength variables, the determination coefficient R2 of prediction set and RMSEP (root mean squared error of prediction) are 0.970 8 and 0.125 4 respectively, equivalent to that of 132 wavelengths, however, the number of wavelength variables was reduced to 16.67%. This algorithm is deterministic iteration, without complex parameters setting and randomicity of variable selection, and its physical significance was well defined. The modeling using a few selected characteristic wavelength variables which affected modeling effect heavily, instead of total spectrum, can make the complexity of model decreased, meanwhile the robustness of model improved. The research offered important reference for developing special oil near infrared spectroscopy analysis instruments on next step.

  13. Kalman Filter for Generalized 2-D Roesser Models

    Institute of Scientific and Technical Information of China (English)

    SHENG Mei; ZOU Yun

    2007-01-01

    The design problem of the state filter for the generalized stochastic 2-D Roesser models, which appears when both the state and measurement are simultaneously subjected to the interference from white noise, is discussed. The wellknown Kalman filter design is extended to the generalized 2-D Roesser models. Based on the method of "scanning line by line", the filtering problem of generalized 2-D Roesser models with mode-energy reconstruction is solved. The formula of the optimal filtering, which minimizes the variance of the estimation error of the state vectors, is derived. The validity of the designed filter is verified by the calculation steps and the examples are introduced.

  14. Reduction of power line interference in electrocardiographic signals by dual Kalman filtering

    Directory of Open Access Journals (Sweden)

    Luis David Avendaño Valencia

    2010-04-01

    Full Text Available This paper presents a filter for reducing powerline interference in electrocardiographic signals (ECG, based on dual parameter and state estimation using with a Kalman filter. Two models were used to represent power-line interference and ECG signal. Both models were combined to simulate the ECG signal whose state was estimated for separating the ECG signal from the interference. The proposed algorithm was fine-tuned and compared using a set of tests relying on the QT arrhythmia database. Tuning tests were done for tracking clean ECG; these results were used for setting the algorithm’s parameters for later filtering tests. Exhaustive filtering tests were carried out on artificially corrupted database registers for given signal to noise ratios; performance curves were thus obtained, leading to comparing the proposed algorithm with other filtering methods. The proposed algorithm was compared to an recursive infinite impulse response filter (IIR and a Kalman filter based on a simpler model. A filtering algorithm was thus obtained which is robust for changes in interference amplitude and keeps these properties for different types of ECG morphologies.

  15. The Unscented Kalman Filter estimates the plasma insulin from glucose measurement.

    Science.gov (United States)

    Eberle, Claudia; Ament, Christoph

    2011-01-01

    Understanding the simultaneous interaction within the glucose and insulin homeostasis in real-time is very important for clinical treatment as well as for research issues. Until now only plasma glucose concentrations can be measured in real-time. To support a secure, effective and rapid treatment e.g. of diabetes a real-time estimation of plasma insulin would be of great value. A novel approach using an Unscented Kalman Filter that provides an estimate of the current plasma insulin concentration is presented, which operates on the measurement of the plasma glucose and Bergman's Minimal Model of the glucose insulin homeostasis. We can prove that process observability is obtained in this case. Hence, a successful estimator design is possible. Since the process is nonlinear we have to consider estimates that are not normally distributed. The symmetric Unscented Kalman Filter (UKF) will perform best compared to other estimator approaches as the Extended Kalman Filter (EKF), the simplex Unscented Kalman Filter (UKF), and the Particle Filter (PF). The symmetric UKF algorithm is applied to the plasma insulin estimation. It shows better results compared to the direct (open loop) estimation that uses a model of the insulin subsystem.

  16. Parallelized unscented Kalman filters for carrier recovery in coherent optical communication.

    Science.gov (United States)

    Jignesh, Jokhakar; Corcoran, Bill; Lowery, Arthur

    2016-07-15

    We show that unscented Kalman filters can be used to mitigate local oscillator phase noise and to compensate carrier frequency offset in coherent single-carrier optical communication systems. A parallel processing architecture implementing the unscented Kalman filter is proposed, improving upon a previous parallelized linear Kalman filter (LKF) implementation.

  17. An improved self-adaptive Kalman filter algorithm and its application in integrated navigation systems for AUV%改进的自适应Kalman滤波方法及其在AUV组合导航中的应用

    Institute of Scientific and Technical Information of China (English)

    孙玉山; 李岳明; 万磊; 庞永杰

    2013-01-01

    针对自主水下机器人(AUV)的工作特点与执行水下作业任务时对导航的需求,构建了基于航位推算的AUV组合导航系统体系结构,建立了水下机器人运动方程与观测方程,采用自适应卡尔曼滤波对水下机器人传感器信息进行数据处理.针对自适应卡尔曼滤波方法的缺点,采取渐消记忆指数加权方法引入了遗忘因子,并采用预报残差的方法求解最佳遗忘因子,同时采取措施保证了系统噪声估计方差阵和测量噪声估计方差阵的半正定性和正定性,避免了滤波发散现象.海试实验结果表明,改进的自适应卡尔曼滤波具有良好的滤波效果,可以满足水下机器人执行各种作业任务的水下导航定位精度.%According to the working characteristics of autonomous underwater vehicles (AUVs) and their navigation requirements when performing underwater tasks, the architecture of integrated navigation systems for AUVs based on dead-reckoning was designed. The motion equation and the observation equation of underwater vehicles were constructed , and a self-adaptive Kalman filter was adopted for processing the data from underwater vehicles' sensors. To overcome the disadvantages of the self-adaptive Kalman filter, the forgetting factor was introduced based on the fading exponent method, and the residual prediction algorithm was used for computing the optimal forgetting factor. And some measures were taken to ensure the half positive of the matrix of system noisy estimation and the positive of the matrix of measure noisy estimation, which can avoid divergence. The sea experimental results show that the improved self-adaptive Kalman filter method is effective, and can meet the AUVs' demand in navigation and positioning when they carry out underwater missions.

  18. Noise Removing of Audio Speech Signals by Means of Kalman Filter

    Directory of Open Access Journals (Sweden)

    Soleyman Shirzadi

    2016-06-01

    Full Text Available Nowadays, multimedia (audio and video processing is among the most important subjects discussed in engineering sciences. To apply digital filters, especially adapting filters, in the above process, are of crucial importance. Theory of adapting filters such as that of Wiener or Kalman, have been fully discussed within the continuous field, the same as in discrete-time one; in spite of this, due to the presence of computers and digital processors, the adaptable filters defiantly have more efficiency in continuous field rather than discrete-time filed. One digital filter along with an adaptable algorithm is usually applied in adaptable filters so that the filter factor can be determined by means of adaptable algorithm. In the present article the Kalman filter-which counts as one of the best filters- has been surveyed whose appropriate factors is being calculated to design a efficient filter. First of all a sample signal is randomly selected which can be the same as an Autoregressive signal. Then a merely random Gaussian noise is applied on Autoregressive signal; and consequently the noisy signal is analyzed. As soon as we analyze the noise removed. The aforesaid operation has been assimilated through the Matlab software. The results have been demonstrated as well.

  19. Automated septum thickness measurement--a Kalman filter approach.

    Science.gov (United States)

    Snare, Sten Roar; Mjølstad, Ole Christian; Orderud, Fredrik; Dalen, Håvard; Torp, Hans

    2012-11-01

    Interventricular septum thickness in end-diastole (IVSd) is one of the key parameters in cardiology. This paper presents a fast algorithm, suitable for pocket-sized ultrasound devices, for measurement of IVSd using 2D B-mode parasternal long axis images. The algorithm is based on a deformable model of the septum and the mitral valve. The model shape is estimated using an extended Kalman filter. A feasibility study using 32 unselected recordings is presented. The recordings originate from a database consisting of subjects from a normal healthy population. Five patients with suspected hypertrophy were included in the study. Reference B-mode measurements were made by two cardiologists. A paired t-test revealed a non-significant mean difference, compared to the B-mode reference, of (mean±SD) 0.14±1.36 mm (p=0.532). Pearson's correlation coefficient was 0.79 (p<0.001). The results are comparable to the variability between the two cardiologists, which was found to be 1.29±1.23 mm (p<0.001). The results indicate that the method has potential as a tool for rapid assessment of IVSd. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  20. Kalman filtering and Standard Quantum Limits for broadband measurement

    CERN Document Server

    Mabuchi, H

    1998-01-01

    I utilize the Caves-Milburn model for continuous position measurements to formulate a broadband version of the Standard Quantum Limit (SQL) for monitoring the position of a free mass, and illustrate the use of Kalman filtering to recover the SQL for estimating a weak classical force that acts on a quantum-mechanical test particle under continuous observation.

  1. Forecasting with the Standardized Self-Perturbed Kalman Filter

    DEFF Research Database (Denmark)

    Grassi, Stefano; Nonejad, Nima; Santucci de Magistris, Paolo

    compared to other on-line, classical and Bayesian methods. The standardized self-perturbed Kalman filter is adopted to forecast the equity premium on the S&P500 index under several model specifications, and to investigate to what extent and how realized variance can be exploited to predict excess returns....

  2. Kalman filter techniques for accelerated Cartesian dynamic cardiac imaging.

    Science.gov (United States)

    Feng, Xue; Salerno, Michael; Kramer, Christopher M; Meyer, Craig H

    2013-05-01

    In dynamic MRI, spatial and temporal parallel imaging can be exploited to reduce scan time. Real-time reconstruction enables immediate visualization during the scan. Commonly used view-sharing techniques suffer from limited temporal resolution, and many of the more advanced reconstruction methods are either retrospective, time-consuming, or both. A Kalman filter model capable of real-time reconstruction can be used to increase the spatial and temporal resolution in dynamic MRI reconstruction. The original study describing the use of the Kalman filter in dynamic MRI was limited to non-Cartesian trajectories because of a limitation intrinsic to the dynamic model used in that study. Here the limitation is overcome, and the model is applied to the more commonly used Cartesian trajectory with fast reconstruction. Furthermore, a combination of the Kalman filter model with Cartesian parallel imaging is presented to further increase the spatial and temporal resolution and signal-to-noise ratio. Simulations and experiments were conducted to demonstrate that the Kalman filter model can increase the temporal resolution of the image series compared with view-sharing techniques and decrease the spatial aliasing compared with TGRAPPA. The method requires relatively little computation, and thus is suitable for real-time reconstruction.

  3. Reducing Support Vector Machine Classification Error by Implementing Kalman Filter

    Directory of Open Access Journals (Sweden)

    Muhsin Hassan

    2013-08-01

    Full Text Available The aim of this is to demonstrate the capability of Kalman Filter to reduce Support Vector Machine classification errors in classifying pipeline corrosion depth. In pipeline defect classification, it is important to increase the accuracy of the SVM classification so that one can avoid misclassification which can lead to greater problems in monitoring pipeline defect and prediction of pipeline leakage. In this paper, it is found that noisy data can greatly affect the performance of SVM. Hence, Kalman Filter + SVM hybrid technique has been proposed as a solution to reduce SVM classification errors. The datasets has been added with Additive White Gaussian Noise in several stages to study the effect of noise on SVM classification accuracy. Three techniques have been studied in this experiment, namely SVM, hybrid of Discrete Wavelet Transform + SVM and hybrid of Kalman Filter + SVM. Experiment results have been compared to find the most promising techniques among them. MATLAB simulations show Kalman Filter and Support Vector Machine combination in a single system produced higher accuracy compared to the other two techniques.

  4. Nonlinear Kalman filtering in the presence of additive noise

    Science.gov (United States)

    Kraszewski, Tomasz; Czopik, Grzegorz

    2017-04-01

    Each modern navigation or localization system designed for ground, water or air objects should provide information on the estimated parameters continuously and as accurately as possible. The implementation of such a process requires the application to operate on non-linear transformations. The defined expectations necessitate the use of nonlinear filtering elements with particular emphasis on the extended Kalman filter. The article presents the simulation research elements of this filter type in the aspect of the possibility of its practical implementation. In the initial phase of the study the conclusion was based on nonlinear one-dimensional model. The possibility of improving the precision of the output through the use of unscented Kalman filters was also analyzed.

  5. Noise adaptive fading Kalman filter for free-space laser communication beacon tracking.

    Science.gov (United States)

    Li, Lixing; Huang, Yongmei; Wang, Qiang; Yang, Fasheng

    2016-10-20

    We proposed a prediction algorithm for laser communication pointing, acquisition, and tracking (PAT) subsystems in order to further improve PAT accuracy and reduce the effect of processing delay. In terms of this prediction algorithm, a fading Kalman filter is employed, with the observation noise obtained by the gray value distribution of the laser images. Moreover, to better fit the dynamics of a laser target, the two-stage dynamic model has been chosen as the state transition model for Kalman filtering. In addition, the two-stage dynamic model has been modified by accommodating its form to a change of time lag, thereby compensating the effect of time delay. A series of horizontal path (17 km) experiments under different atmospheric conditions were conducted in the fields. According to the experimental results, the algorithm we proposed could effectively reduce the tracking error and improve pointing accuracy.

  6. 基于Kalman预测和K-近邻的多目标跟踪%Tracking of Muti-objects Base on Algorithm of Kalman Filter and Knn

    Institute of Scientific and Technical Information of China (English)

    曹作宝; 包晓敏; 汪亚明; 周砚江

    2011-01-01

    In this paper, directing at the strong points and weak points of Kalman filter based on tracking method, a novel approach to tracking of muti-objects is studied. By using Kalman-filter, the researchers predict locations where objects most probably appear in a next-frame, determine the search radius and k, calculate the shortest Euclidean distance with the predicted target object in this radius by using k-nearest neighbor, and then determine the true target location. Based on the MATLAB simulation environment to achieve the tracking algorithm, experimental results show that tracking results and performance is better. In addition, selecting a reasonable k values, can reduce the computation and speed up the system processing speed.%针对Kalman预测跟踪和K-近邻数据关联算法的优缺点,研究一种基于Kalman预测和K-近邻的多目标跟踪方法.该方法首先利用Kalman滤波预测出运动目标在下一帧中最可能出现的位置,接着根据当前帧目标位置和预测目标位置的距离,确定搜索半径,利用K-近邻数据关联算法,在该半径范围内,计算与预测点欧式距离最短的目标,并将其确定为真实目标位置.在MATLAB仿真环境下实现该跟踪算法,实验结果表明,用该方法进行多目标跟踪时,跟踪效果和性能较为稳定和可靠.此外选择合理的K值,能减少运算量,加快系统处理速度.

  7. MEMS IMU/GNSS 联邦卡尔曼滤波中的基于四元数的约束算法%Quaternion based constrained algorithm in federated Kalman filtering for MEMS IMU/GNSS

    Institute of Scientific and Technical Information of China (English)

    刘华; 刘彤

    2013-01-01

      当全球导航卫星系统(GNSS)失效时,微硅机械(MEMS)惯性测量单元(IMU)与GNSS 组合而成的导航系统性能会下降。针对于陆地车辆的导航应用,建立了一个联邦卡尔曼滤波器,四元数是其中一个局部滤波器的部分待估计状态。四元数所得到推算的沿车辆机体坐标系的加速度约束扩展了滤波器的观测量。车载试验表明,与传统滤波算法相比,使用该算法可使三维位置导航精度在 GNSS 信号失效30 s 时提高25%,姿态和速度精度也相应的提高。%  The performance of MEMS IMU/GNSS integrated system would degrade with outages of GNSS signal. For land vehicle applications, a federated Kalman filter was established. The quaternion was employed as part of states in one of the local filters. The acceleration constraint derived from quaternion was applied along the vehicle’s body frame to expand observables in the filter. The field test shows that, compared with conventional filtering algorithms, the proposed algorithm brings 25% performance improvement in three-dimension(3D) positioning during 30 s GNSS outages. The accuracy of attitude and velocity is improved as well.

  8. Evaluation of two modified Kalman gain algorithms for radar data assimilation in the WRF model

    Directory of Open Access Journals (Sweden)

    Chun Yang

    2015-05-01

    Full Text Available This work attempts to validate two modified Kalman gain algorithms by assimilating a single radar simulation data set into the Weather Research and Forecasting model using an Ensemble Square Root Filter. Emphasis is placed on the comparison of assimilation performance between the two modified algorithms against the classical Kalman gain algorithm when the measurement operator is non-linear. Three ideal storm-scale experiments, which are configured identically except for the different Kalman gain algorithms, are designed in parallel for this purpose. The results show that the first modified algorithm can result in a better simulation of a storm, as measured by the root mean square error (RMSE. The second algorithm can also, to some extent, reduce the RMSE of the simulation of some state vectors, but with little improvement of the estimation of storm intensity. Overall, our preliminary experiments indicate that the two modified Kalman gain algorithms can benefit the assimilation of complex numerical models when the measurement operators are non-linear, confirming the earlier theoretical analysis and the results of simple models. Further work is needed to evaluate the impact of the modified Kalman gain algorithms on the assimilation performance of ensemble-based methods.

  9. Autonomous orbit determination via Kalman filtering of gravity gradients

    OpenAIRE

    Sun; Chen,De; Macabiau, Christophe; Han

    2016-01-01

    International audience; Spaceborne gravity gradients are proposed in this paper to provide autonomous orbit determination capabilities for near Earth satellites. The gravity gradients contain useful position information which can be extracted by matching the observations with a precise gravity model. The extended Kalman filter is investigated as the principal estimator. The stochastic model of orbital motion, the measurement equation and the model configuration are discussed for the filter de...

  10. On Ensemble Nonlinear Kalman Filtering with Symmetric Analysis Ensembles

    KAUST Repository

    Luo, Xiaodong

    2010-09-19

    The ensemble square root filter (EnSRF) [1, 2, 3, 4] is a popular method for data assimilation in high dimensional systems (e.g., geophysics models). Essentially the EnSRF is a Monte Carlo implementation of the conventional Kalman filter (KF) [5, 6]. It is mainly different from the KF at the prediction steps, where it is some ensembles, rather then the means and covariance matrices, of the system state that are propagated forward. In doing this, the EnSRF is computationally more efficient than the KF, since propagating a covariance matrix forward in high dimensional systems is prohibitively expensive. In addition, the EnSRF is also very convenient in implementation. By propagating the ensembles of the system state, the EnSRF can be directly applied to nonlinear systems without any change in comparison to the assimilation procedures in linear systems. However, by adopting the Monte Carlo method, the EnSRF also incurs certain sampling errors. One way to alleviate this problem is to introduce certain symmetry to the ensembles, which can reduce the sampling errors and spurious modes in evaluation of the means and covariances of the ensembles [7]. In this contribution, we present two methods to produce symmetric ensembles. One is based on the unscented transform [8, 9], which leads to the unscented Kalman filter (UKF) [8, 9] and its variant, the ensemble unscented Kalman filter (EnUKF) [7]. The other is based on Stirling’s interpolation formula (SIF), which results in the divided difference filter (DDF) [10]. Here we propose a simplified divided difference filter (sDDF) in the context of ensemble filtering. The similarity and difference between the sDDF and the EnUKF will be discussed. Numerical experiments will also be conducted to investigate the performance of the sDDF and the EnUKF, and compare them to a well‐established EnSRF, the ensemble transform Kalman filter (ETKF) [2].

  11. A modified ensemble Kalman particle filter for non-Gaussian systems with nonlinear measurement functions

    Science.gov (United States)

    Shen, Zheqi; Tang, Youmin

    2016-04-01

    The ensemble Kalman particle filter (EnKPF) is a combination of two Bayesian-based algorithms, namely, the ensemble Kalman filter (EnKF) and the sequential importance resampling particle filter(SIR-PF). It was recently introduced to address non-Gaussian features in data assimilation for highly nonlinear systems, by providing a continuous interpolation between the EnKF and SIR-PF analysis schemes. In this paper, we first extend the EnKPF algorithm by modifying the formula for the computation of the covariancematrix, making it suitable for nonlinear measurement functions (we will call this extended algorithm nEnKPF). Further, a general form of the Kalman gain is introduced to the EnKPF to improve the performance of the nEnKPF when the measurement function is highly nonlinear (this improved algorithm is called mEnKPF). The Lorenz '63 model and Lorenz '96 model are used to test the two modified EnKPF algorithms. The experiments show that the mEnKPF and nEnKPF, given an affordable ensemble size, can perform better than the EnKF for the nonlinear systems with nonlinear observations. These results suggest a promising opportunity to develop a non-Gaussian scheme for realistic numerical models.

  12. Ensemble Kalman filtering without the intrinsic need for inflation

    Directory of Open Access Journals (Sweden)

    M. Bocquet

    2011-10-01

    Full Text Available The main intrinsic source of error in the ensemble Kalman filter (EnKF is sampling error. External sources of error, such as model error or deviations from Gaussianity, depend on the dynamical properties of the model. Sampling errors can lead to instability of the filter which, as a consequence, often requires inflation and localization. The goal of this article is to derive an ensemble Kalman filter which is less sensitive to sampling errors. A prior probability density function conditional on the forecast ensemble is derived using Bayesian principles. Even though this prior is built upon the assumption that the ensemble is Gaussian-distributed, it is different from the Gaussian probability density function defined by the empirical mean and the empirical error covariance matrix of the ensemble, which is implicitly used in traditional EnKFs. This new prior generates a new class of ensemble Kalman filters, called finite-size ensemble Kalman filter (EnKF-N. One deterministic variant, the finite-size ensemble transform Kalman filter (ETKF-N, is derived. It is tested on the Lorenz '63 and Lorenz '95 models. In this context, ETKF-N is shown to be stable without inflation for ensemble size greater than the model unstable subspace dimension, at the same numerical cost as the ensemble transform Kalman filter (ETKF. One variant of ETKF-N seems to systematically outperform the ETKF with optimally tuned inflation. However it is shown that ETKF-N does not account for all sampling errors, and necessitates localization like any EnKF, whenever the ensemble size is too small. In order to explore the need for inflation in this small ensemble size regime, a local version of the new class of filters is defined (LETKF-N and tested on the Lorenz '95 toy model. Whatever the size of the ensemble, the filter is stable. Its performance without inflation is slightly inferior to that of LETKF with optimally tuned inflation for small interval between updates, and

  13. Ensemble Kalman filtering in presence of inequality constraints

    Science.gov (United States)

    van Leeuwen, P. J.

    2009-04-01

    Kalman filtering is presence of constraints is an active area of research. Based on the Gaussian assumption for the probability-density functions, it looks hard to bring in extra constraints in the formalism. On the other hand, in geophysical systems we often encounter constraints related to e.g. the underlying physics or chemistry, which are violated by the Gaussian assumption. For instance, concentrations are always non-negative, model layers have non-negative thickness, and sea-ice concentration is between 0 and 1. Several methods to bring inequality constraints into the Kalman-filter formalism have been proposed. One of them is probability density function (pdf) truncation, in which the Gaussian mass from the non-allowed part of the variables is just equally distributed over the pdf where the variables are alolwed, as proposed by Shimada et al. 1998. However, a problem with this method is that the probability that e.g. the sea-ice concentration is zero, is zero! The new method proposed here does not have this drawback. It assumes that the probability-density function is a truncated Gaussian, but the truncated mass is not distributed equally over all allowed values of the variables, but put into a delta distribution at the truncation point. This delta distribution can easily be handled with in Bayes theorem, leading to posterior probability density functions that are also truncated Gaussians with delta distributions at the truncation location. In this way a much better representation of the system is obtained, while still keeping most of the benefits of the Kalman-filter formalism. In the full Kalman filter the formalism is prohibitively expensive in large-scale systems, but efficient implementation is possible in ensemble variants of the kalman filter. Applications to low-dimensional systems and large-scale systems will be discussed.

  14. Research on Pruning Algorithm in Neural Networks Based on Kalman Filter%基于卡尔曼滤波的神经网络修剪算法研究

    Institute of Scientific and Technical Information of China (English)

    吴静; 刘衍珩; 吕荣

    2009-01-01

    传统的BP神经网络在应用的过程中,常常面临无法确定合适的网络节点问题.网络规模小,则运算时间长;而网络规模过大,容易产生过学习现象,影响泛化能力.在传统的BP神经网络学习的基础上,采用卡尔曼滤波箅法对神经网络中的权值向量进行修剪,实现对神经网络结构的简化,提高泛化能力.它不同于以往的边修剪、边训练,而是在神经网络一次完整的学习完成之后,一次性修剪.方法在入侵检测数据集测试中表明,修剪比例较高,精确度好,修剪完成的网络能够很好地保持修剪前的测试识别率,提高了学习速度和泛化能力.%In the application of traditional BP neural networks, a problem often faced is how to determine the appropriate numbers of the neurons. If the scale of the network is too small, it will cause long training time. On the contrary, if the scale is too big, the networks will lead to over fitting which plays an important role to generalization ability of NN. In this paper, a Kalman Filter algorithm is applied to prune the weights of Neural Networks in order to improve the speed of learning and the generalization ability of Neural Networks. Compared with traditional pruning algorithm, this method is different. While the traditional pruning algorithm prunes while training, this method prunes the weights after a complete training. Tests in IDS dataset show that Kalman Filter algorithm can prune with a higher rate and accuracy. Furthermore, the pruned Neural Networks can keep the detection rate of unpruned ones.

  15. Application of the Kalman Filter to Estimate the State of an Aerobraking Maneuver

    Directory of Open Access Journals (Sweden)

    Willer Gomes dos Santos

    2013-01-01

    Full Text Available This paper presents a study about the application of a Kalman filter to estimate the position and velocity of a spacecraft in an aerobraking maneuver around the Earth. The cis-lunar aerobraking of the Hiten spacecraft as well as an aerobraking in a LEO orbit are simulated in this paper. The simulator developed considers a reference trajectory and a trajectory perturbed by external disturbances combined with nonidealities of sensors and actuators. It is able to operate in closed loop controlling the trajectory at each instant of time using a PID controller and propulsive jets. A Kalman filter utilizes the sensor data to estimate the state of the spacecraft. The estimation algorithms and propagation equations used in this process are presented. The U.S. Standard Atmosphere is adopted as the atmospheric model. The main results are compared with the case where the Kalman filter is not used. Therefore, it was possible to perform an analysis of the Kalman filter importance applied to an aerobraking maneuver.

  16. Development of Real-Time Error Ellipses as an Indicator of Kalman Filter Performance.

    Science.gov (United States)

    1984-03-01

    S q often than 3 to 5 seconds. However, before the HP-86 can e considered feasible for real-time Kalman filtr procssinz, more investigaz ion i: needi...Subtitle) S. TYPE OF REPORT & PERIOD COVERED Development of Real-Time Error Master’s Thesis; Ellipses as an Indicator of Kalman March 1984 Filter...SUPP.LEETARY NOTES 19. KEY WORDS (Cmntine on reveo ole, It ndeeaey md Identil by block number) Error Ellipsoids; Kalman Filter; Extended Kalman Filter

  17. Research on Signal Fusion Technology of Angle Measurement Based on Kalman Filtering Algorithm%基于Kalman滤波算法角度测量信号融合技术研究

    Institute of Scientific and Technical Information of China (English)

    张立中; 胡瀚元; 洪进; 孟立新; 白杨杨

    2015-01-01

    针对车载激光通信转台姿态角控制中陀螺仪传感器存在漂移误差等问题,提出了以陀螺仪传感器和加速度传感器作为测量元件对车载转台的姿态角进行测量的方法,并在MATLAB软件平台上建立卡尔曼滤波器将传感器采集的信号进行融合,以加速度传感器输出的姿态角信息对陀螺仪测量的姿态角进行修正、补偿,以提高姿态角测量的精度。通过建立试验系统完成测试,结果表明:应用Kalman滤波算法对陀螺仪和加速度传感器信号进行融合后,有效地减小姿态角的测量误差,为准确获得转台姿态角信息提供了理论依据。%Aiming at the drift error problems of attitude angle of gyroscope sensor in the vehicular laser communicate turntable, in this paper, gyroscope and accelerometer are used as measuring elements to measure the attitude angle of turntable. The attitude angles output by gyroscope is modified and compensated by the attitude angle output of acceler-ometer. Kalman filter is established in MATLAB software platform to fuse the collected sensor signal, so the attitude angle measurement accuracy is improved. The experiment test system is establised to complete the test,the experimen-tal results show that the measurement error of attitude angle can be effectively reduced and the dynamic characteristics of attitude angle measurement is dramatically improved after using Kalman filtering algorithm to fuse gyroscope and ac-celerometer signal,a theoretical basis is precisely provided for obtaining turntable attitude angle information.

  18. Kalman filter algorithm for short-term jam traffic prediction based on traffic parameter correlation%基于交通流参数相关的阻塞流短时预测卡尔曼滤波算法

    Institute of Scientific and Technical Information of China (English)

    董春娇; 邵春福; 周雪梅; 孟梦; 诸葛承祥

    2014-01-01

    提出一种考虑交通流参数相关关系的卡尔曼滤波算法,实现阻塞流状态下道路网交通流短时预测.在交通流守恒方程的基础上,借鉴偏微分方程求解Lax-Wendroff格式离散的思想,结合阻塞流状态下交通流时间和空间特性及进出口匝道等因素的影响,建立阻塞流状态下交通流短时预测状态空间模型,并设计基于卡尔曼滤波方法的模型求解算法.最后以北京市某一区域路网为例,进行了实证性研究.研究结果表明:所建立的阻塞流状态下交通流短时预测卡尔曼滤波算法由于同时考虑了时间和空间因素,能够使预测平均绝对百分比误差(MAPE)控制在10%以内;平均 MAPE 仅为7.96%.相同条件下,ARIMA 模型和 Elman 模型预测 MAPE 分别为19.88%和10.51%.%A Kalman filter model considering the correlation property of traffic flow parameters is proposed to realize network short-term traffic flow prediction under jam traffic.The proposed state-space model of short-term traffic flow prediction is presented by solving the conservation equation u-sing Lax-Wendroff scheme.In addition,the spatial-temporal characteristics of the traffic flow on ur-ban expressway networks and the influence factors of on and off ramp are taken into account for flow rate prediction.The estimation algorithm of the proposed state-space model is designed based on the Kalman filter method.A region expressway network in Beijing is taken as an example to evaluate the performance of the proposed method.The results show that the maximum prediction mean absolute percentage error(MAPE)of the proposed Kalman filter model is less than 10%since the input of the Kalman filter model considers the impacts of spatial-temporal characteristics,and the mean of predic-tion MAPE is 7.96%.For the same predicted conditions,the mean prediction MAPEs of ARIMA and Elman model are 19.88% and 10.5 1%,respectively.

  19. Kalman Filter for Estimation of Sensor Acceleration Using Six - axis Inertial Sensor

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jung Keun [Hankyong National University, Anseong (Korea, Republic of)

    2015-02-15

    Although an accelerometer is a sensor that measures acceleration, it cannot be used by itself to measure the acceleration when the orientation of the sensor changes. This paper introduces a Kalman filter for the estimation of a sensor acceleration based on a six-axis inertial sensor (i.e., a three-axis accelerometer and three-axis gyroscope). The novelty of the proposed Kalman filter lies in the fact that its state vector includes not only the tilt angle variable but also the sensor acceleration. Thus, the filter can explicitly estimate the latter with a high accuracy. The accuracy of acceleration estimates were validated experimentally under three different dynamic conditions, using an optical motion capture system. It could be concluded that the performance of the proposed Kalman filter was comparable to that of the state-of-the-art estimation algorithm employed by the Xsens MTw. The proposed algorithm may be more suitable than inertial/magnetic sensor-based algorithms for various applications adopting six-axis inertial sensors.

  20. Multi-sensor Data Processing and Fusing Based on Kalman Filtering

    Directory of Open Access Journals (Sweden)

    Bian Guangrong

    2013-01-01

    Full Text Available The background of this paper is the warehouse target localization and tracking system which is composed of a number of wireless sensor nodes. Firstly this paper established a model of warehouse target localization and tracking system, then a model of multi-sensor data preprocessing and data fusion was established, and self-adaptive linear recursive method was used to eliminate outliers of the original measured data. Then least squares fitting filter was used to do filtering and denoising for the measured data. In the end, the data which were measured by multi-sensor can be fused by Kalman Filtering algorithm. Data simulation analysis shows that the use of kalman filtering algorithm for the fusion of the data measured by multi-sensor is to obtain more accurate warehouse target location data, so as to increase the positioning and tracking accuracy of the warehouse target localization and tracking system. Key Words:Wireless Sensor Network,Data Fusion,Kalman Filtering

  1. Fault estimation of satellite reaction wheels using covariance based adaptive unscented Kalman filter

    Science.gov (United States)

    Rahimi, Afshin; Kumar, Krishna Dev; Alighanbari, Hekmat

    2017-05-01

    Reaction wheels, as one of the most commonly used actuators in satellite attitude control systems, are prone to malfunction which could lead to catastrophic failures. Such malfunctions can be detected and addressed in time if proper analytical redundancy algorithms such as parameter estimation and control reconfiguration are employed. Major challenges in parameter estimation include speed and accuracy of the employed algorithm. This paper presents a new approach for improving parameter estimation with adaptive unscented Kalman filter. The enhancement in tracking speed of unscented Kalman filter is achieved by systematically adapting the covariance matrix to the faulty estimates using innovation and residual sequences combined with an adaptive fault annunciation scheme. The proposed approach provides the filter with the advantage of tracking sudden changes in the system non-measurable parameters accurately. Results showed successful detection of reaction wheel malfunctions without requiring a priori knowledge about system performance in the presence of abrupt, transient, intermittent, and incipient faults. Furthermore, the proposed approach resulted in superior filter performance with less mean squared errors for residuals compared to generic and adaptive unscented Kalman filters, and thus, it can be a promising method for the development of fail-safe satellites.

  2. Robust Ensemble Filtering and Its Relation to Covariance Inflation in the Ensemble Kalman Filter

    KAUST Repository

    Luo, Xiaodong

    2011-12-01

    A robust ensemble filtering scheme based on the H∞ filtering theory is proposed. The optimal H∞ filter is derived by minimizing the supremum (or maximum) of a predefined cost function, a criterion different from the minimum variance used in the Kalman filter. By design, the H∞ filter is more robust than the Kalman filter, in the sense that the estimation error in the H∞ filter in general has a finite growth rate with respect to the uncertainties in assimilation, except for a special case that corresponds to the Kalman filter. The original form of the H∞ filter contains global constraints in time, which may be inconvenient for sequential data assimilation problems. Therefore a variant is introduced that solves some time-local constraints instead, and hence it is called the time-local H∞ filter (TLHF). By analogy to the ensemble Kalman filter (EnKF), the concept of ensemble time-local H∞ filter (EnTLHF) is also proposed. The general form of the EnTLHF is outlined, and some of its special cases are discussed. In particular, it is shown that an EnKF with certain covariance inflation is essentially an EnTLHF. In this sense, the EnTLHF provides a general framework for conducting covariance inflation in the EnKF-based methods. Some numerical examples are used to assess the relative robustness of the TLHF–EnTLHF in comparison with the corresponding KF–EnKF method.

  3. A cascaded two-step Kalman filter for estimation of human body segment orientation using MEMS-IMU.

    Science.gov (United States)

    Zihajehzadeh, S; Loh, D; Lee, M; Hoskinson, R; Park, E J

    2014-01-01

    Orientation of human body segments is an important quantity in many biomechanical analyses. To get robust and drift-free 3-D orientation, raw data from miniature body worn MEMS-based inertial measurement units (IMU) should be blended in a Kalman filter. Aiming at less computational cost, this work presents a novel cascaded two-step Kalman filter orientation estimation algorithm. Tilt angles are estimated in the first step of the proposed cascaded Kalman filter. The estimated tilt angles are passed to the second step of the filter for yaw angle calculation. The orientation results are benchmarked against the ones from a highly accurate tactical grade IMU. Experimental results reveal that the proposed algorithm provides robust orientation estimation in both kinematically and magnetically disturbed conditions.

  4. Weighted ensemble transform Kalman filter for image assimilation

    Directory of Open Access Journals (Sweden)

    Sebastien Beyou

    2013-01-01

    Full Text Available This study proposes an extension of the Weighted Ensemble Kalman filter (WEnKF proposed by Papadakis et al. (2010 for the assimilation of image observations. The main focus of this study is on a novel formulation of the Weighted filter with the Ensemble Transform Kalman filter (WETKF, incorporating directly as a measurement model a non-linear image reconstruction criterion. This technique has been compared to the original WEnKF on numerical and real world data of 2-D turbulence observed through the transport of a passive scalar. In particular, it has been applied for the reconstruction of oceanic surface current vorticity fields from sea surface temperature (SST satellite data. This latter technique enables a consistent recovery along time of oceanic surface currents and vorticity maps in presence of large missing data areas and strong noise.

  5. An aperiodic phenomenon of the unscented Kalman filter in filtering noisy chaotic signals

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A non-periodic oscillatory behavior of the unscented Kalman filter (UKF) when used to filter noisy contaminated chaotic signals is reported. We show both theoretically and experimentally that the gain of the UKF may not converge or diverge but oscillate aperiodically. More precisely, when a nonlinear system is periodic, the Kalman gain and error covariance of the UKF converge to zero. However, when the system being considered is chaotic, the Kalman gain either converges to a fixed point with a magnitude larger than zero or oscillates aperiodically.

  6. Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

    Science.gov (United States)

    Xia, Youshen; Wang, Jun

    2015-07-01

    This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction.

  7. 一种新型自适应联邦滤波方法在战术导弹导航系统中的应用%Model noise Kalman filter algorithm for electro-optical tracking

    Institute of Scientific and Technical Information of China (English)

    刘英; 田蔚风; 赵健康; 鲍其莲; 朱士青

    2012-01-01

    In order to reduce the sensitivity of the filtering performance to the model noise in the target state space model, the method of updating the system noise covariance and measurement noise variance was proposed. By combining it with nonlinear Kalman filter, an adaptive nonlinear Kalman filter was constituted which is suitable for applying in electro-optical target tracking. Meanwhile it was applied in nonlinear measurement electro-optical tracking system, and compared with those of extended Kalman filter and anscented Kalman filter. The Matlab simulation results show that this method can adjust measurement noise covariance and system noise covariance in real time, and can effectively avoid the problem of filter performance degradation caused by the inaccurately statistical properties of the measurement noise and system noise, and the performance by the model noise Kalman filter significantly outperforms those of the extended Kalman filter and the unscented Kalman filter.%为满足复杂的环境下战术导弹导航系统的高可靠性导航的要求,对战术导弹的多传感器组合导航进行了研究.提出了一种基于新型自适应联邦卡尔曼滤波的巡航导弹SINS/GPS/EC组合导航方法,根据联邦滤波的分散滤波结构,分别建立了各滤波器的模型,进行了仿真试验验证.仿真结果表明,采用新型自适应联邦卡尔曼滤波算法的导航精度比采用集中卡尔曼滤波算法提高幅度不大,略高一些,但从自适应联邦卡尔曼滤波器的容错性比集中卡尔曼滤波器好得多,便于各导航子系统的故障检查和隔离.本文设计自适应联邦SINS/GPS/EC滤波器的在子系统较多的组合导航设计中具有高可靠性、低计算量、低成本和小体积等优势,具有工程应用价值.

  8. Adaptive error covariances estimation methods for ensemble Kalman filters

    Energy Technology Data Exchange (ETDEWEB)

    Zhen, Yicun, E-mail: zhen@math.psu.edu [Department of Mathematics, The Pennsylvania State University, University Park, PA 16802 (United States); Harlim, John, E-mail: jharlim@psu.edu [Department of Mathematics and Department of Meteorology, The Pennsylvania State University, University Park, PA 16802 (United States)

    2015-08-01

    This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for using information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example.

  9. Bds/gps Integrated Positioning Method Research Based on Nonlinear Kalman Filtering

    Science.gov (United States)

    Ma, Y.; Yuan, W.; Sun, H.

    2017-09-01

    In order to realize fast and accurate BDS/GPS integrated positioning, it is necessary to overcome the adverse effects of signal attenuation, multipath effect and echo interference to ensure the result of continuous and accurate navigation and positioning. In this paper, pseudo-range positioning is used as the mathematical model. In the stage of data preprocessing, using precise and smooth carrier phase measurement value to promote the rough pseudo-range measurement value without ambiguity. At last, the Extended Kalman Filter(EKF), the Unscented Kalman Filter(UKF) and the Particle Filter(PF) algorithm are applied in the integrated positioning method for higher positioning accuracy. The experimental results show that the positioning accuracy of PF is the highest, and UKF is better than EKF.

  10. Accounting for model error due to unresolved scales within ensemble Kalman filtering

    CERN Document Server

    Mitchell, Lewis

    2014-01-01

    We propose a method to account for model error due to unresolved scales in the context of the ensemble transform Kalman filter (ETKF). The approach extends to this class of algorithms the deterministic model error formulation recently explored for variational schemes and extended Kalman filter. The model error statistic required in the analysis update is estimated using historical reanalysis increments and a suitable model error evolution law. Two different versions of the method are described; a time-constant model error treatment where the same model error statistical description is time-invariant, and a time-varying treatment where the assumed model error statistics is randomly sampled at each analysis step. We compare both methods with the standard method of dealing with model error through inflation and localization, and illustrate our results with numerical simulations on a low order nonlinear system exhibiting chaotic dynamics. The results show that the filter skill is significantly improved through th...

  11. Kalman filter of the force signal of identifying weld seam in remote teaching

    Institute of Scientific and Technical Information of China (English)

    Liu Lijun; Zhu Ronghua; Zhang Guangjun; Gao Hongming; Wu Lin

    2008-01-01

    For reasons of the vibration of robot, the rough surface of weld seam and electromagnetic disturbance of welding machine, the force signals of identifying weld seam become unstable. The position error of remote teaching point is too big to meet teaching requirements in remote welding. The force signals of identifying weld seam can be filtered by Kalman. The force signals of identifying weld seam of next teaching point is accurately predicted according to predicting algorithms, such as the equation of the state, the equation of the observation, the gain matrix of the filter and the covariance matrix of predicting state. The experimental results show that the precision of identifying weld seam is improved by Kalman filter.

  12. A cognition-based method to ease the computational load for an extended Kalman filter.

    Science.gov (United States)

    Li, Yanpeng; Li, Xiang; Deng, Bin; Wang, Hongqiang; Qin, Yuliang

    2014-12-03

    The extended Kalman filter (EKF) is the nonlinear model of a Kalman filter (KF). It is a useful parameter estimation method when the observation model and/or the state transition model is not a linear function. However, the computational requirements in EKF are a difficulty for the system. With the help of cognition-based designation and the Taylor expansion method, a novel algorithm is proposed to ease the computational load for EKF in azimuth predicting and localizing under a nonlinear observation model. When there are nonlinear functions and inverse calculations for matrices, this method makes use of the major components (according to current performance and the performance requirements) in the Taylor expansion. As a result, the computational load is greatly lowered and the performance is ensured. Simulation results show that the proposed measure will deliver filtering output with a similar precision compared to the regular EKF. At the same time, the computational load is substantially lowered.

  13. Model Predictive Control Based on Kalman Filter for Constrained Hammerstein-Wiener Systems

    Directory of Open Access Journals (Sweden)

    Man Hong

    2013-01-01

    Full Text Available To precisely track the reactor temperature in the entire working condition, the constrained Hammerstein-Wiener model describing nonlinear chemical processes such as in the continuous stirred tank reactor (CSTR is proposed. A predictive control algorithm based on the Kalman filter for constrained Hammerstein-Wiener systems is designed. An output feedback control law regarding the linear subsystem is derived by state observation. The size of reaction heat produced and its influence on the output are evaluated by the Kalman filter. The observation and evaluation results are calculated by the multistep predictive approach. Actual control variables are computed while considering the constraints of the optimal control problem in a finite horizon through the receding horizon. The simulation example of the CSTR tester shows the effectiveness and feasibility of the proposed algorithm.

  14. Quaternion normalization in additive EKF for spacecraft attitude determination. [Extended Kalman Filters

    Science.gov (United States)

    Bar-Itzhack, I. Y.; Deutschmann, J.; Markley, F. L.

    1991-01-01

    This work introduces, examines and compares several quaternion normalization algorithms, which are shown to be an effective stage in the application of the additive extended Kalman filter to spacecraft attitude determination, which is based on vector measurements. Three new normalization schemes are introduced. They are compared with one another and with the known brute force normalization scheme, and their efficiency is examined. Simulated satellite data are used to demonstate the performance of all four schemes.

  15. Container spreader attitude estimation using cubature Kalman filtering algorithm%用容积卡尔曼滤波算法进行集装箱吊具姿态估计

    Institute of Scientific and Technical Information of China (English)

    李胜永; 季本山; 张智华; 张江南

    2015-01-01

    为提高非线性模型下集装箱吊具姿态的估计精度,建立一个基于四元数和低精度高噪声传感器的集装箱吊具姿态估计非线性模型,运用容积卡尔曼滤波算法进行姿态估计。试验数据与高精度姿态传感器数据比较表明,采用容积卡尔曼滤波算法可以有效提高集装箱吊具姿态估计精度和稳定性。%In order to improve the estimation accuracy of container spreader attitude in a nonlinear mod-el,a nonlinear model based on the quaternion and the sensor with low accuracy and high noise is estab-lished to estimate the container spreader attitude by the Cubature Kalman Filtering (CKF)algorithm. Through comparing the test data with the high-accuracy attitude sensor data,it shows that the CKF algo-rithm can effectively improve the accuracy and stability of the container spreader attitude estimation.

  16. Streamflow Data Assimilation in SWAT Model Using Extended Kalman Filter

    Science.gov (United States)

    Sun, L.; Nistor, I.; Seidou, O.

    2014-12-01

    Although Extended Kalman Filter (EKF) is regarded as the de facto method for the application of Kalman Filter in non-linear system, it's application to complex distributed hydrological models faces a lot of challenges. Ensemble Kalman Filter (EnKF) is often preferred because it avoids the calculation of the linearization Jacobian Matrix and the propagation of estimation error covariance. EnKF is however difficult to apply to large models because of the huge computation demand needed for parallel propagation of ensemble members. This paper deals with the application of EKF in stream flow prediction using the SWAT model in the watershed of Senegal River, West Africa. In the Jacobian Matrix calculation, SWAT is regarded as a black box model and the derivatives are calculated in the form of differential equations. The state vector is the combination of runoff, soil, shallow aquifer and deep aquifer water contents. As an initial attempt, only stream flow observations are assimilated. Despite the fact that EKF is a sub-optimal filter, the coupling of EKF significantly improves the estimation of daily streamflow. The results of SWAT+EKF are also compared to those of a simpler quasi linear streamflow prediction model where both state and parameters are updated with the EKF.

  17. Acoustic cardiac signals analysis: a Kalman filter-based approach.

    Science.gov (United States)

    Salleh, Sheik Hussain; Hussain, Hadrina Sheik; Swee, Tan Tian; Ting, Chee-Ming; Noor, Alias Mohd; Pipatsart, Surasak; Ali, Jalil; Yupapin, Preecha P

    2012-01-01

    Auscultation of the heart is accompanied by both electrical activity and sound. Heart auscultation provides clues to diagnose many cardiac abnormalities. Unfortunately, detection of relevant symptoms and diagnosis based on heart sound through a stethoscope is difficult. The reason GPs find this difficult is that the heart sounds are of short duration and separated from one another by less than 30 ms. In addition, the cost of false positives constitutes wasted time and emotional anxiety for both patient and GP. Many heart diseases cause changes in heart sound, waveform, and additional murmurs before other signs and symptoms appear. Heart-sound auscultation is the primary test conducted by GPs. These sounds are generated primarily by turbulent flow of blood in the heart. Analysis of heart sounds requires a quiet environment with minimum ambient noise. In order to address such issues, the technique of denoising and estimating the biomedical heart signal is proposed in this investigation. Normally, the performance of the filter naturally depends on prior information related to the statistical properties of the signal and the background noise. This paper proposes Kalman filtering for denoising statistical heart sound. The cycles of heart sounds are certain to follow first-order Gauss-Markov process. These cycles are observed with additional noise for the given measurement. The model is formulated into state-space form to enable use of a Kalman filter to estimate the clean cycles of heart sounds. The estimates obtained by Kalman filtering are optimal in mean squared sense.

  18. Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs

    Science.gov (United States)

    Cerati, Giuseppe; Elmer, Peter; Krutelyov, Slava; Lantz, Steven; Lefebvre, Matthieu; Masciovecchio, Mario; McDermott, Kevin; Riley, Daniel; Tadel, Matevž; Wittich, Peter; Würthwein, Frank; Yagil, Avi

    2017-08-01

    For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as Graphical Processing Units (GPU), ARM CPUs, and Intel MICs. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particle tracks during event reconstruction. This is expected to become by far the dominant problem at the High-Luminosity Large Hadron Collider (HL-LHC), for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offine. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port the Kalman filter to NVIDIA GPUs.

  19. Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs

    Directory of Open Access Journals (Sweden)

    Cerati Giuseppe

    2017-01-01

    Full Text Available For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as Graphical Processing Units (GPU, ARM CPUs, and Intel MICs. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particle tracks during event reconstruction. This is expected to become by far the dominant problem at the High-Luminosity Large Hadron Collider (HL-LHC, for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offine. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port the Kalman filter to NVIDIA GPUs.

  20. Multivariable frequency response methods for optimal Kalman-Bucy filters with applications to radar tracking systems

    Science.gov (United States)

    Arcasoy, C. C.

    1992-11-01

    The problem of multi-output, infinite-time, linear time-invariant optimal Kalman-Bucy filter both in continuous and discrete-time cases in frequency domain is addressed. A simple new algorithm is given for the analytical solution to the steady-state gain of the optimum filter based on a transfer function approach. The algorithm is based on spectral factorization of observed spectral density matrix of the filter which generates directly the return-difference matrix of the optimal filter. The method is more direct than by algebraic Riccati equation solution and can easily be implemented on digital computer. The design procedure is illustrated by examples and closed-form solution of ECV and ECA radar tracking filters are considered as an application of the method.

  1. DYNAMIC ESTIMATION FOR PARAMETERS OF INTERFERENCE SIGNALS BY THE SECOND ORDER EXTENDED KALMAN FILTERING

    Directory of Open Access Journals (Sweden)

    P. A. Ermolaev

    2014-03-01

    Full Text Available Data processing in the interferometer systems requires high-resolution and high-speed algorithms. Recurrence algorithms based on parametric representation of signals execute consequent processing of signal samples. In some cases recurrence algorithms make it possible to increase speed and quality of data processing as compared with classic processing methods. Dependence of the measured interferometer signal on parameters of its model and stochastic nature of noise formation in the system is, in general, nonlinear. The usage of nonlinear stochastic filtering algorithms is expedient for such signals processing. Extended Kalman filter with linearization of state and output equations by the first vector parameters derivatives is an example of these algorithms. To decrease approximation error of this method the second order extended Kalman filtering is suggested with additionally usage of the second vector parameters derivatives of model equations. Examples of algorithm implementation with the different sets of estimated parameters are described. The proposed algorithm gives the possibility to increase the quality of data processing in interferometer systems in which signals are forming according to considered models. Obtained standard deviation of estimated amplitude envelope does not exceed 4% of the maximum. It is shown that signal-to-noise ratio of reconstructed signal is increased by 60%.

  2. Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration

    Science.gov (United States)

    Meng, Yang; Gao, Shesheng; Zhong, Yongmin; Hu, Gaoge; Subic, Aleksandar

    2016-03-01

    The use of the direct filtering approach for INS/GNSS integrated navigation introduces nonlinearity into the system state equation. As the unscented Kalman filter (UKF) is a promising method for nonlinear problems, an obvious solution is to incorporate the UKF concept in the direct filtering approach to address the nonlinearity involved in INS/GNSS integrated navigation. However, the performance of the standard UKF is dependent on the accurate statistical characterizations of system noise. If the noise distributions of inertial instruments and GNSS receivers are not appropriately described, the standard UKF will produce deteriorated or even divergent navigation solutions. This paper presents an adaptive UKF with noise statistic estimator to overcome the limitation of the standard UKF. According to the covariance matching technique, the innovation and residual sequences are used to determine the covariance matrices of the process and measurement noises. The proposed algorithm can estimate and adjust the system noise statistics online, and thus enhance the adaptive capability of the standard UKF. Simulation and experimental results demonstrate that the performance of the proposed algorithm is significantly superior to that of the standard UKF and adaptive-robust UKF under the condition without accurate knowledge on system noise, leading to improved navigation precision.

  3. Robust tracking with spatio-velocity snakes: Kalman filtering approach

    Energy Technology Data Exchange (ETDEWEB)

    Peterfreund, N.

    1998-12-31

    Using results from robust Kalman filtering, we present a new Kalman filter-based snake model for tracking of nonrigid objects in combined spatio-velocity space. The proposed model is the stochastic version of the velocity snake, an active contour model for combined tracking of position and velocity of nonrigid boundaries. The proposed model uses image gradient and optical flow measurements along the contour as system measurements. An optical-flow based measurement error is used to detect and reject image measurements which correspond to image clutter or to other objects. The method was applied to object tracking of both rigid and nonrigid objects, resulting in good tracking results and robustness to image clutter, occlusions and numerical noise. 19 refs., 3 figs.

  4. Robust tracking with spatio-velocity snakes: Kalman filtering approach

    Energy Technology Data Exchange (ETDEWEB)

    Peterfreund, N.

    1997-06-01

    Using results from robust Kalman filtering, the author presents a new Kalman filter-based snake model for tracking of nonrigid objects in combined spatio-velocity space. The proposed model is the stochastic version of the velocity snake, an active contour model for combined tracking of position and velocity of nonrigid boundaries. The proposed model uses image gradient and optical flow measurements along the contour as system measurements. An optical-flow based measurement error is used to detect and reject image measurements which correspond to image clutter or to other objects. The method was applied to object tracking of both rigid and nonrigid objects, resulting in good tracking results and robustness to image clutter, occlusions and numerical noise.

  5. Spacecraft Dynamics Should be Considered in Kalman Filter Attitude Estimation

    Science.gov (United States)

    Yang, Yaguang; Zhou, Zhiqiang

    2016-01-01

    Kalman filter based spacecraft attitude estimation has been used in some high-profile missions and has been widely discussed in literature. While some models in spacecraft attitude estimation include spacecraft dynamics, most do not. To our best knowledge, there is no comparison on which model is a better choice. In this paper, we discuss the reasons why spacecraft dynamics should be considered in the Kalman filter based spacecraft attitude estimation problem. We also propose a reduced quaternion spacecraft dynamics model which admits additive noise. Geometry of the reduced quaternion model and the additive noise are discussed. This treatment is more elegant in mathematics and easier in computation. We use some simulation example to verify our claims.

  6. Randomized Filtering Algorithms

    DEFF Research Database (Denmark)

    Katriel, Irit; Van Hentenryck, Pascal

    2008-01-01

    of AllDifferent and is generalization, the Global Cardinality Constraint. The first delayed filtering scheme is a Monte Carlo algorithm: its running time is superior, in the worst case, to that of enforcing are consistency after every domain event, while its filtering effectiveness is analyzed...... in the expected sense. The second scheme is a Las Vegas algorithm using filtering triggers: Its effectiveness is the same as enforcing are consistency after every domain event, while in the expected case it is faster by a factor of m/n, where n and m are, respectively, the number of nodes and edges...

  7. Object Tracking System Using Approximate Median Filter, Kalman Filter and Dynamic Template Matching

    Directory of Open Access Journals (Sweden)

    G. Mallikarjuna Rao

    2014-04-01

    Full Text Available In this work, we dealt with the tracking of single object in a sequence of frames either from a live camera or a previously saved video. A moving object is detected frame-by-frame with high accuracy and efficiency using Median approximation technique. As soon as the object has been detected, the same is tracked by kalman filter estimation technique along with a more accurate Template Matching algorithm. The templates are dynamically generated for this purpose. This guarantees any change in object pose which does not be hindered from tracking procedure. The system is capable of handling entry and exit of an object. Such a tracking scheme is cost effective and it can be used as an automated video conferencing system and also has application as a surveillance tool. Several trials of the tracking show that the approach is correct and extremely fast, and it's a more robust performance throughout the experiments.

  8. Multiplicative Quaternion Extended Kalman Filtering for Nonspinning Guided Projectiles

    Science.gov (United States)

    2013-07-01

    micro- electromechanical system ( MEMS ) gyroscopes have been able to measure the spin-rates of fin- stabilized projectiles such as mortars, which...model, the statistics of the gyroscope and accelerometer noise are measureable, and can be easily incorporated into an extended Kalman filtering...tradeoff between affordability, durability, and performance. Automotive-grade MEMS components have been used in the harsh gun-launch environment for

  9. Robust Optical User Motion Tracking Using a Kalman Filter

    OpenAIRE

    Dorfmüller-Ulhaas, Klaus

    2007-01-01

    Optical tracking has a great future in applications of virtual and augmented reality. It will assist to enhance the acceptance of virtual reality user interfaces, since optical tracking allows wireless interaction and precise tracking. Existing commercial motion capture systems are neither working reliably in real-time. Additionally, only few optical trackers can smooth and predict motion and include a motion estimator supplying similar results to the presented approach. A Kalman filter formu...

  10. Adaptive high-gain extended kalman filter and applications

    OpenAIRE

    Boizot, Nicolas Richard

    2010-01-01

    The work concerns the ``observability problem” --- the reconstruction of a dynamic process's full state from a partially measured state--- for nonlinear dynamic systems. The Extended Kalman Filter (EKF) is a widely-used observer for such nonlinear systems. However it suffers from a lack of theoretical justifications and displays poor performance when the estimated state is far from the real state, e.g. due to large perturbations, a poor initial state estimate, etc… We propose a solution to...

  11. Localization of Wheeled Mobile Robot Based on Extended Kalman Filtering

    Directory of Open Access Journals (Sweden)

    Li Guangxu

    2015-01-01

    Full Text Available A mobile robot localization method which combines relative positioning with absolute orientation is presented. The code salver and gyroscope are used for relative positioning, and the laser radar is used to detect absolute orientation. In this paper, we established environmental map, multi-sensor information fusion model, sensors and robot motion model. The Extended Kalman Filtering (EKF is adopted as multi-sensor data fusion technology to realize the precise localization of wheeled mobile robot.

  12. Improvements of Analog Neural Networks Based on Kalman Filter

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2002-04-01

    Full Text Available In the paper, original improvements of recurrent analog neuralnetworks, which are based on Kalman filter, are presented. Theseimprovements eliminate some disadvantages of the classical Kalmanneural network and enable a real time processing of quickly changingsignals, which appear in adaptive antennas and similar applications.This goal is reached using such circuit elements, which increase theconvergence rate of the network and decrease the dependence ofconvergence rate on the ratio of eigenvalues of the correlation matrixof input signals.

  13. SUBSTANTIATION OF THE PUBLIC DEBT SUSTAINABILITY USING KALMAN FILTER

    Directory of Open Access Journals (Sweden)

    Bolos Marcel

    2011-12-01

    Full Text Available Global economic conditions have pushed many countries into the delicate situation of contracting foreign loans, leading overnight at alarming volumes of public debt. The need for control and relevant analysis for the sustainability of a country's public debt has led us to use the Kalman filter in predicting future values of the key indicators of public debt. The development of a mathematical model of analysis for public services and the budget deficit was necessary to objectively assess the level of the public debt sustainability.Knowing future values of the public debt or the future evolutions of the revenues for the operational budget, offers the posibility of a better handling of the operational expenditures and finally a better balance for the public budget deficit.Using the mathematical mechanism of Kalman filters implemented in Matlab programming language, we generated the estimated future values of the proposed model proposed and key indicators, the results confirming the fears of a low public debt sustainability for Romania.We predicted the future values for the debt service, the public external debt and the operational public revenues,expenditures and deficit, and compared them, to obtain an image of the future evolution and position of the sustainability of the public debt. The work in this paper is an innovative one for the public science sector, and the results obtained are promising for future researches. The values estimated by the Kalman filter are an orientation for the future public policies, and indicate a rather stable but negative evolution for the public debt service. The sustainability of the public debt depends on the decisions taken for the correction of the estimated values, in changing the negative evolution of the budgetary indicators into a positive one.Taking all this into consideration we will conclude that the mathematical mecanism of the Kalman filters offers valuable informations for Government and future

  14. Three-dimensional motion tracking by Kalman filtering

    Science.gov (United States)

    Gao, Jean; Kosaka, Akio; Kak, Avinash C.

    2000-10-01

    In this paper, a 3D semantic object motion tracking method based on Kalman filtering is proposed. First, we use a specially designed Color Image Segmentation Editor (CISE) to devise shapes that more accurately describe the object to be tracked. CISE is an integration of edge and region detection, which is based on edge-linking, split-and-merge and the energy minimization for active contour detection. An ROI is further segmented into single motion blobs by considering the constancy of the motion parameters in each blob. Over short time intervals, each blob can be tracked separately and, over longer times, the blobs can be allowed to fragment and coalesce into new blobs as motion evolves. The tracking of each blob is based on a Kalman filter derived from linearization of a constraint equation satisfied by the pinhole model of a camera. The Kalman filter allows the tracker to project the uncertainties associated with a blob center (or with the coordinates of any other features) into the next frame. This projected uncertainty region can then be searched rot eh pixels belonging to the blob. Future work includes investigation of the effects of illumination changes and simultaneous tracking of multiple targets.

  15. Relationship between Allan variances and Kalman Filter parameters

    Science.gov (United States)

    Vandierendonck, A. J.; Mcgraw, J. B.; Brown, R. G.

    1984-01-01

    A relationship was constructed between the Allan variance parameters (H sub z, H sub 1, H sub 0, H sub -1 and H sub -2) and a Kalman Filter model that would be used to estimate and predict clock phase, frequency and frequency drift. To start with the meaning of those Allan Variance parameters and how they are arrived at for a given frequency source is reviewed. Although a subset of these parameters is arrived at by measuring phase as a function of time rather than as a spectral density, they all represent phase noise spectral density coefficients, though not necessarily that of a rational spectral density. The phase noise spectral density is then transformed into a time domain covariance model which can then be used to derive the Kalman Filter model parameters. Simulation results of that covariance model are presented and compared to clock uncertainties predicted by Allan variance parameters. A two state Kalman Filter model is then derived and the significance of each state is explained.

  16. Power grid integration using Kalman filtering

    OpenAIRE

    Djerf, Magnus

    2012-01-01

    Renewable power sources with a relatively uneven or constant DC power production require synchronization methods to work with the current utility power grid. The solution to this synchronization problem has been solved with semiconductor based converters and advanced switching algorithms. To enable switching algorithms that work well with the grids amplitude, phase-shift and frequency, the current waveform has to be measured and estimated.    There are many sources of noise that will add dist...

  17. An adaptive Kalman filter for ECG signal enhancement.

    Science.gov (United States)

    Vullings, Rik; de Vries, Bert; Bergmans, Jan W M

    2011-04-01

    The ongoing trend of ECG monitoring techniques to become more ambulatory and less obtrusive generally comes at the expense of decreased signal quality. To enhance this quality, consecutive ECG complexes can be averaged triggered on the heartbeat, exploiting the quasi-periodicity of the ECG. However, this averaging constitutes a tradeoff between improvement of the SNR and loss of clinically relevant physiological signal dynamics. Using a bayesian framework, in this paper, a sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the averaging based on the characteristics of the ECG signal. The filter has the form of an adaptive Kalman filter. The adaptive estimation of the process and measurement noise covariances is performed by maximizing the bayesian evidence function of the sequential ECG estimation and by exploiting the spatial correlation between several simultaneously recorded ECG signals, respectively. The noise covariance estimates thus obtained render the filter capable of ascribing more weight to newly arriving data when these data contain morphological variability, and of reducing this weight in cases of no morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To gauge the relevance of the adaptive noise-covariance estimation, the performance of the filter is compared to that of a Kalman filter with fixed, (a posteriori) optimized noise covariance. This comparison demonstrates that, without using a priori knowledge on signal characteristics, the filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance, favoring the adaptive filter in cases where no a priori information is available or where signal characteristics are expected to fluctuate.

  18. A COMPARISON OF TWO METHODS FADING MEMORY FILTER AND ADAPTIVE KALMAN FILTER IN MONITORING CRUSTAL MOVEMENT

    Directory of Open Access Journals (Sweden)

    Cahit Tağı ÇELİK

    2004-01-01

    Full Text Available Monitoring the Crustal Movement in Geodesy is performed by the deformation survey and analysis. If monitoring the crustal movements involves more than two epochs of survey campaign then from the plate tectonic theory, stations do not move randomly from one epoch to the other, therefore Kalman Filter may be suitable to use. However, if sudden movements happened in the crust in particular earthquake happened, the crust moves very fast in a very short period of time. When Kalman Filter used for monitoring these movements, from associated epoch, for a number of epochs the results may be biased. In the paper, comparison of two methods for elimination of the above mentioned biases have been performed. These methods are Fading Memory Filter and Adaptive Kalman Filter for an unknown bias.

  19. Kalman filtering for time-delayed linear systems

    Institute of Scientific and Technical Information of China (English)

    LU Xiao; WANG Wei

    2006-01-01

    This paper is to study the linear minimum variance estimation for discrete- time systems. A simple approach to the problem is presented by developing re-organized innovation analysis for the systems with instantaneous and double time-delayed measurements. It is shown that the derived estimator involves solving three different standard Kalman filtering with the same dimension as the original system. The obtained results form the basis for solving some complicated problems such as H∞ fixed-lag smoothing, preview control, H∞ filtering and control with time delays.

  20. 基于自适应衰减因子Kalman滤波的GPS相位平滑伪距算法%Algorithm of GPS phase smoothing pseudo-range based on adaptive attenuation factor Kalman filtering

    Institute of Scientific and Technical Information of China (English)

    崔法毅; 解文肖

    2015-01-01

    载波相位平滑伪距的主要目的是通过高精度的载波相位测量值作为辅助量,使伪距测量值中、大随机误差得以消减。针对GPS伪距测量中未知时变的噪声,提出基于极大后验时变噪声统计估计器的自适应衰减因子Kalman滤波算法(AFKF),采用衰减的加权因子,使估计器逐渐忘记陈旧数据的作用,同时增加新数据的比重,避免滤波过程的发散。结合载波相位平滑伪距原理,利用AFKF算法对全球导航卫星系统(GNSS)的国际 GNSS 服务组织(IGS)的跟踪站实测数据进行仿真分析,并提出利用伪距双差值及伪距三差值来直观体现不同算法的效果比较,结果表明:与标准Kalman滤波相比,AFKF算法在伪距平滑应用中取得很好的效果。%The main purpose of carrier phase smoothing pseudo- range is to reduce large random error of pseudo- range measurement values, by using high- precision carrier phase measurement values as the supplementary information. In view of the unknown time - varying noise in GPS pseudo - range measurement, an algorithm of adaptive attenuation factor kalman filter (AFKF) was put forward, which was based on maximum a posteriori (MAP) time- varying noise statistical estimator. In order to avoid the divergence of filtering process, the effect of old data could be gradually forgotten by using estimator with attenuation weighted factors, while the proportion of new data could be increased. Simulation analysis was carried out on the measured data of tracking station of a International Global Navigation Satellite System Service (IGS), by using the AFKF algorithm combining with carrier phase smoothing pseudo- range principle. And the double differential and the three differential pseudo- ranges were proposed to intuitively reflect the effects of different algorithms. Experimental results show that the AFKF algorithm can obtain better effect in application of pseudo- range smoothing, compared

  1. Comparison of Unscented Kalman Filter and Unscented Schmidt Kalman Filter in Predicting Attitude and Associated Uncertainty of a Geosynchronous Satellite

    Science.gov (United States)

    2014-09-01

    attitude estimate. 1. INTRODUCTION The utility of using brightness ( radiometric flux intensity) measurements to determine a space object (SO)’s...a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE SEP...2014 2. REPORT TYPE 3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE Comparison of Unscented Kalman Filter and Unscented Schmidt

  2. Incremental Activation Detection for Real-Time fMRI Series Using Robust Kalman Filter

    Directory of Open Access Journals (Sweden)

    Liang Li

    2014-01-01

    Full Text Available Real-time functional magnetic resonance imaging (rt-fMRI is a technique that enables us to observe human brain activations in real time. However, some unexpected noises that emerged in fMRI data collecting, such as acute swallowing, head moving and human manipulations, will cause much confusion and unrobustness for the activation analysis. In this paper, a new activation detection method for rt-fMRI data is proposed based on robust Kalman filter. The idea is to add a variation to the extended kalman filter to handle the additional sparse measurement noise and a sparse noise term to the measurement update step. Hence, the robust Kalman filter is designed to improve the robustness for the outliers and can be computed separately for each voxel. The algorithm can compute activation maps on each scan within a repetition time, which meets the requirement for real-time analysis. Experimental results show that this new algorithm can bring out high performance in robustness and in real-time activation detection.

  3. A Discontinuous Extended Kalman Filter for non-smooth dynamic problems

    Science.gov (United States)

    Chatzis, M. N.; Chatzi, E. N.; Triantafyllou, S. P.

    2017-08-01

    Problems that result into locally non-differentiable and hence non-smooth state-space equations are often encountered in engineering. Examples include problems involving material laws pertaining to plasticity, impact and highly non-linear phenomena. Estimating the parameters of such systems poses a challenge, particularly since the majority of system identification algorithms are formulated on the basis of smooth systems under the assumption of observability, identifiability and time invariance. For a smooth system, an observable state remains observable throughout the system evolution with the exception of few selected realizations of the state vector. However, for a non-smooth system the observable set of states and parameters may vary during the evolution of the system throughout a dynamic analysis. This may cause standard identification (ID) methods, such as the Extended Kalman Filter, to temporarily diverge and ultimately fail in accurately identifying the parameters of the system. In this work, the influence of observability of non-smooth systems to the performance of the Extended and Unscented Kalman Filters is discussed and a novel algorithm particularly suited for this purpose, termed the Discontinuous Extended Kalman Filter (DEKF), is proposed.

  4. Incremental activation detection for real-time fMRI series using robust Kalman filter.

    Science.gov (United States)

    Li, Liang; Yan, Bin; Tong, Li; Wang, Linyuan; Li, Jianxin

    2014-01-01

    Real-time functional magnetic resonance imaging (rt-fMRI) is a technique that enables us to observe human brain activations in real time. However, some unexpected noises that emerged in fMRI data collecting, such as acute swallowing, head moving and human manipulations, will cause much confusion and unrobustness for the activation analysis. In this paper, a new activation detection method for rt-fMRI data is proposed based on robust Kalman filter. The idea is to add a variation to the extended kalman filter to handle the additional sparse measurement noise and a sparse noise term to the measurement update step. Hence, the robust Kalman filter is designed to improve the robustness for the outliers and can be computed separately for each voxel. The algorithm can compute activation maps on each scan within a repetition time, which meets the requirement for real-time analysis. Experimental results show that this new algorithm can bring out high performance in robustness and in real-time activation detection.

  5. Software Would Largely Automate Design of Kalman Filter

    Science.gov (United States)

    Chuang, Jason C. H.; Negast, William J.

    2005-01-01

    Embedded Navigation Filter Automatic Designer (ENFAD) is a computer program being developed to automate the most difficult tasks in designing embedded software to implement a Kalman filter in a navigation system. The most difficult tasks are selection of error states of the filter and tuning of filter parameters, which are timeconsuming trial-and-error tasks that require expertise and rarely yield optimum results. An optimum selection of error states and filter parameters depends on navigation-sensor and vehicle characteristics, and on filter processing time. ENFAD would include a simulation module that would incorporate all possible error states with respect to a given set of vehicle and sensor characteristics. The first of two iterative optimization loops would vary the selection of error states until the best filter performance was achieved in Monte Carlo simulations. For a fixed selection of error states, the second loop would vary the filter parameter values until an optimal performance value was obtained. Design constraints would be satisfied in the optimization loops. Users would supply vehicle and sensor test data that would be used to refine digital models in ENFAD. Filter processing time and filter accuracy would be computed by ENFAD.

  6. Comparison of Kalman filter and optimal smoother estimates of spacecraft attitude

    Science.gov (United States)

    Sedlak, J.

    1994-01-01

    Given a valid system model and adequate observability, a Kalman filter will converge toward the true system state with error statistics given by the estimated error covariance matrix. The errors generally do not continue to decrease. Rather, a balance is reached between the gain of information from new measurements and the loss of information during propagation. The errors can be further reduced, however, by a second pass through the data with an optimal smoother. This algorithm obtains the optimally weighted average of forward and backward propagating Kalman filters. It roughly halves the error covariance by including future as well as past measurements in each estimate. This paper investigates whether such benefits actually accrue in the application of an optimal smoother to spacecraft attitude determination. Tests are performed both with actual spacecraft data from the Extreme Ultraviolet Explorer (EUVE) and with simulated data for which the true state vector and noise statistics are exactly known.

  7. Thermal Error Modeling of the CNC Machine Tool Based on Data Fusion Method of Kalman Filter

    Directory of Open Access Journals (Sweden)

    Haitong Wang

    2017-01-01

    Full Text Available This paper presents a modeling methodology for the thermal error of machine tool. The temperatures predicted by modified lumped-mass method and the temperatures measured by sensors are fused by the data fusion method of Kalman filter. The fused temperatures, instead of the measured temperatures used in traditional methods, are applied to predict the thermal error. The genetic algorithm is implemented to optimize the parameters in modified lumped-mass method and the covariances in Kalman filter. The simulations indicate that the proposed method performs much better compared with the traditional method of MRA, in terms of prediction accuracy and robustness under a variety of operating conditions. A compensation system is developed based on the controlling system of Siemens 840D. Validated by the compensation experiment, the thermal error after compensation has been reduced dramatically.

  8. Frequency-scanning interferometry for dynamic absolute distance measurement using Kalman filter.

    Science.gov (United States)

    Tao, Long; Liu, Zhigang; Zhang, Weibo; Zhou, Yangli

    2014-12-15

    We propose a frequency-scanning interferometry using the Kalman filtering technique for dynamic absolute distance measurement. Frequency-scanning interferometry only uses a single tunable laser driven by a triangle waveform signal for forward and backward optical frequency scanning. The absolute distance and moving speed of a target can be estimated by the present input measurement of frequency-scanning interferometry and the previously calculated state based on the Kalman filter algorithm. This method not only compensates for movement errors in conventional frequency-scanning interferometry, but also achieves high-precision and low-complexity dynamic measurements. Experimental results of dynamic measurements under static state, vibration and one-dimensional movement are presented.

  9. Applications of Kalman Filtering to nuclear material control. [Kalman filtering and linear smoothing for detecting nuclear material losses

    Energy Technology Data Exchange (ETDEWEB)

    Pike, D.H.; Morrison, G.W.; Westley, G.W.

    1977-10-01

    The feasibility of using modern state estimation techniques (specifically Kalman Filtering and Linear Smoothing) to detect losses of material from material balance areas is evaluated. It is shown that state estimation techniques are not only feasible but in most situations are superior to existing methods of analysis. The various techniques compared include Kalman Filtering, linear smoothing, standard control charts, and average cumulative summation (CUSUM) charts. Analysis results indicated that the standard control chart is the least effective method for detecting regularly occurring losses. An improvement in the detection capability over the standard control chart can be realized by use of the CUSUM chart. Even more sensitivity in the ability to detect losses can be realized by use of the Kalman Filter and the linear smoother. It was found that the error-covariance matrix can be used to establish limits of error for state estimates. It is shown that state estimation techniques represent a feasible and desirable method of theft detection. The technique is usually more sensitive than the CUSUM chart in detecting losses. One kind of loss which is difficult to detect using state estimation techniques is a single isolated loss. State estimation procedures are predicated on dynamic models and are well-suited for detecting losses which occur regularly over several accounting periods. A single isolated loss does not conform to this basic assumption and is more difficult to detect.

  10. Performance analysis of improved iterated cubature Kalman filter and its application to GNSS/INS.

    Science.gov (United States)

    Cui, Bingbo; Chen, Xiyuan; Xu, Yuan; Huang, Haoqian; Liu, Xiao

    2017-01-01

    In order to improve the accuracy and robustness of GNSS/INS navigation system, an improved iterated cubature Kalman filter (IICKF) is proposed by considering the state-dependent noise and system uncertainty. First, a simplified framework of iterated Gaussian filter is derived by using damped Newton-Raphson algorithm and online noise estimator. Then the effect of state-dependent noise coming from iterated update is analyzed theoretically, and an augmented form of CKF algorithm is applied to improve the estimation accuracy. The performance of IICKF is verified by field test and numerical simulation, and results reveal that, compared with non-iterated filter, iterated filter is less sensitive to the system uncertainty, and IICKF improves the accuracy of yaw, roll and pitch by 48.9%, 73.1% and 83.3%, respectively, compared with traditional iterated KF.

  11. 基于BP神经网络的自主定轨自适应Kalman滤波算法%An Adaptive Kalman Filtering Algorithm for Autonomous Orbit Determination Based-on BP Neural Network

    Institute of Scientific and Technical Information of China (English)

    尚琳; 刘国华; 张锐; 李国通

    2013-01-01

    针对Sage-Husa自适应滤波方法存在的窗函数开窗大小选择问题,提出一种基于BP神经网络学习估计系统协方差矩阵的自适应Kalman滤波算法.该算法以Kalman滤波预测残差向量作为网络输入,通过网络分段离线学习确定预测残差向量与预测残差协方差矩阵间的非线性关系,自适应地估计Kalman滤波系统协方差矩阵.将其应用到自主定轨系统,仿真结果表明利用本文算法自主定轨60天星座平均URE误差小于1.9米,且能够快速跟踪到系统噪声的突变,较Kalman滤波方法和Sage-Husa自适应滤波方法具有更好的性能.%In this paper,an adaptive covariance matrix estimation algorithm based-on BP neural network learning is proposed to solve the window size selection problem in Sage-Husa adaptive filtering way.The innovation vector derived from the Kalman filter (KF) is employed as the input to the BP neural network and the nonlinear function between the innovation vector and the innovation covariance matrix can be determined through learning of the network.The covariance matrix estimation algorithm proposed in this paper is applied to the autonomous orbit determination system.The simulation results show that the mean constellation URE of autonomous orbit determination will be within 1.9 meters in 60 days and it has better performance than the Sage-Husa adaptive filtering in the estimation of the system covariance matrix of the autonomous orbit determination algorithm.

  12. VLBI real-time analysis by Kalman Filtering

    Science.gov (United States)

    Karbon, Maria; Soja, Benedikt; Nilson, Tobias; Heinkelmann, Robert; Liu, Li; Lu, Ciuxian; Xu, Minghui; Raposo-Pulido, Virginia; Mora-Diaz, Julian; Schuh, Harald

    2014-05-01

    Geodetic Very Long Baseline Interferometry (VLBI) is one of the primary space geodetic techniques. It provides the full set of Earth Orientation Parameter (EOP) and is unique for observing long term Universal Time (UT1) and precession/nutation. Currently the VLBI products are delivered with a delay of about two weeks from the moment of the observation. However, the need for near-real time estimates of the parameters is increasing, e.g. for satellite based navigation and positioning or for enabling precise tracking of interplanetary spacecraft. The goal is thus to reduce the time span between observation and the final result to less than one day. This can be archived by replacing the classical least squares method with an adaptive Kalman filter. We have developed a Kalman filter for VLBI data analysis. This method has the advantage that it is simultaneously possible to estimate stationary parameters, e.g. station positions, and to model the highly variable stochastic behavior of non-stationary parameters like clocks or atmospheric parameters. The filter is able to perform without any human interaction, making it a completely autonomous tool. In this work we describe the filter and discuss its application for EOP determination and prediction. We discuss the implementation of the stochastic models to statistically account for unpredictable changes in EOP. Furthermore, additional data like results from other techniques can be included to improve the performance. For example, atmospheric angular momentum calculated from numerical weather models can be introduced to supplement the short-term prediction of UT1 and polar motion. This Kalman filter will be extended and embedded in the newly developed Vienna VLBI Software (VieVS) as a completely autonomous tool enabling the VLBI analysis in near real-time and providing all the parameters of interest with the highest possible accuracy.

  13. 基于卡尔曼滤波的橘小实蝇成虫运动轨迹优化跟踪%Moving trace optimization tracking for adult of Bactrocera Dorsalis (Hendel) based on Kalman filter algorithm

    Institute of Scientific and Technical Information of China (English)

    文韬; 洪添胜; 李立君; 张南峰; 李震; 郭鑫

    2014-01-01

    Bactrocera Dorsalis (Hendel) are invasive pests which occur frequently and are seriously harmful to the growth of fruit trees, and they have been ranked an important quarantine object in many countries and regions. The regular manual survey used as the routine predicting method for Bactrocera Dorsalis (Hendel) has not accomplished the requirement of real-time and precise monitoring and warning by means of the adult trapping and monitoring device deployed in orchards. With the development of science and technologies, the method of the automatic machine monitoring for pests has been studied including detection of sound characteristics, radar monitoring and spectral monitoring. Considering the characteristic with randomness, migratory and hiding for Bactrocera Dorsalis (Hendel), there were some problems such as timing, processing and costs in monitoring pests with the aid of combining the above monitoring and the traditional method. In order to accomplish precise monitoring for Bactrocera Dorsalis (Hendel), machine vision technologies were used as an in-field automatic detecting method for the Hendel adults in this paper. Considering the problem with tracking Bactrocera Dorsalis (Hendel) object disappearance in multi-objects with more closer condition by means of the mean shift algorithm in color space according to previous machine vision technology research results, the fusion algorithm based on mean shift and Kalman filter theories for moving objects was proposed for optimizing multi-objects moving trace tracking by means of colorful analysis for moving objects and background in monitoring zones. The recurrence relation of the adults moving trace was obtained, and position coordinate, X-component and Y-component of speed in the 2D plane were extracted by image processing and matching technologies in this algorithm. By analyzing the state sequence linear minimum variance estimate theory of dynamic system and recurrence relation of the adults moving trace, the model

  14. Vehicle longitudinal velocity estimation during the braking process using unknown input Kalman filter

    Science.gov (United States)

    Moaveni, Bijan; Khosravi Roqaye Abad, Mahdi; Nasiri, Sayyad

    2015-10-01

    In this paper, vehicle longitudinal velocity during the braking process is estimated by measuring the wheels speed. Here, a new algorithm based on the unknown input Kalman filter is developed to estimate the vehicle longitudinal velocity with a minimum mean square error and without using the value of braking torque in the estimation procedure. The stability and convergence of the filter are analysed and proved. Effectiveness of the method is shown by designing a real experiment and comparing the estimation result with actual longitudinal velocity computing from a three-axis accelerometer output.

  15. 基于自适应UKF算法的MEMS陀螺空中在线标定技术%In-flight on-line calibration method for MEMS gyroscope based on adaptive unscented Kalman filter algorithm

    Institute of Scientific and Technical Information of China (English)

    秦伟; 苑伟政; 常洪龙; 薛亮

    2011-01-01

    The attitude sensors need to be calibrated on-line in order to guarantee the performance of system in the application of the micro-satellite.A real-time drift error estimation method of MEMS gyroscope is presented by using three-axis magnetometer measurements without any external attitude reference.The unscented Kalman filter (UKF) is applied as the optimal estimation algorithm, the gyroscope drift is used as the filter state vector, and the finite difference of magnetometers observation is established as the measurement vector.Since the measurements of the magnetometers are susceptible to interferences, and this would lead to inaccuracy of the filter model, the adaptive UKF is applied to decrease the drift estimation errors of the gyroscope by monitoring the measurements vectors and adjusting its covariance matrix on-line.The experiment results indicate that the accuracy of the calibrated MEMS gyroscopes has improved about 30%, and the filter parameters are adjusted automatically when the magnetometer measurements are deteriorated, which make the filter convergence.Furthermore, the accuracy of dynamic attitude computed by the calibrated MEMS gyroscope is smaller than 2°.%为保证微型卫星定位应用中系统精度与稳定性,需要对姿态传感器进行实时在线标定.在无外界姿态参考时,提出一种用三轴磁强计测量值来实时估计MEMs陀螺的零漂误差的方法,采用UKF滤波算法,将陀螺漂移作为滤波状态向量,通过建立三轴磁强计测量微分方程,作为系统量测方程实现陀螺漂移的最优估计.针对磁强计测量信息易受干扰导致滤波量测模型不准确的问题,将自适应因子引入到UKF中,通过在线监控和调整测量误差,减少陀螺标定的估计误差,增强系统性能.实验结果表明,经过标定,MEMS陀螺精度提高约30%,并且在磁强计有外界干扰时,陀螺的标定结果收敛.将标定后的MEMS陀螺进行姿态解算,其动态误差小于2°.

  16. Data assimilation the ensemble Kalman filter

    CERN Document Server

    Evensen, Geir

    2007-01-01

    Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. Rather than emphasize a particular discipline such as oceanography or meteorology, it presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should b...

  17. Mean-field Ensemble Kalman Filter

    KAUST Repository

    Law, Kody

    2015-01-07

    A proof of convergence of the standard EnKF generalized to non-Gaussian state space models is provided. A density-based deterministic approximation of the mean-field limiting EnKF (MFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for d < 2 . The fidelity of approximation of the true distribution is also established using an extension of total variation metric to random measures. This is limited by a Gaussian bias term arising from non-linearity/non-Gaussianity of the model, which arises in both deterministic and standard EnKF. Numerical results support and extend the theory.

  18. ECG Denoising Using Marginalized Particle Extended Kalman Filter With an Automatic Particle Weighting Strategy.

    Science.gov (United States)

    Hesar, Hamed Danandeh; Mohebbi, Maryam

    2017-05-01

    In this paper, a model-based Bayesian filtering framework called the "marginalized particle-extended Kalman filter (MP-EKF) algorithm" is proposed for electrocardiogram (ECG) denoising. This algorithm does not have the extended Kalman filter (EKF) shortcoming in handling non-Gaussian nonstationary situations because of its nonlinear framework. In addition, it has less computational complexity compared with particle filter. This filter improves ECG denoising performance by implementing marginalized particle filter framework while reducing its computational complexity using EKF framework. An automatic particle weighting strategy is also proposed here that controls the reliance of our framework to the acquired measurements. We evaluated the proposed filter on several normal ECGs selected from MIT-BIH normal sinus rhythm database. To do so, artificial white Gaussian and colored noises as well as nonstationary real muscle artifact (MA) noise over a range of low SNRs from 10 to -5 dB were added to these normal ECG segments. The benchmark methods were the EKF and extended Kalman smoother (EKS) algorithms which are the first model-based Bayesian algorithms introduced in the field of ECG denoising. From SNR viewpoint, the experiments showed that in the presence of Gaussian white noise, the proposed framework outperforms the EKF and EKS algorithms in lower input SNRs where the measurements and state model are not reliable. Owing to its nonlinear framework and particle weighting strategy, the proposed algorithm attained better results at all input SNRs in non-Gaussian nonstationary situations (such as presence of pink noise, brown noise, and real MA). In addition, the impact of the proposed filtering method on the distortion of diagnostic features of the ECG was investigated and compared with EKF/EKS methods using an ECG diagnostic distortion measure called the "Multi-Scale Entropy Based Weighted Distortion Measure" or MSEWPRD. The results revealed that our proposed

  19. Stochastic Optimal Estimation with Fuzzy Random Variables and Fuzzy Kalman Filtering

    Institute of Scientific and Technical Information of China (English)

    FENG Yu-hu

    2005-01-01

    By constructing a mean-square performance index in the case of fuzzy random variable, the optimal estimation theorem for unknown fuzzy state using the fuzzy observation data are given. The state and output of linear discrete-time dynamic fuzzy system with Gaussian noise are Gaussian fuzzy random variable sequences. An approach to fuzzy Kalman filtering is discussed. Fuzzy Kalman filtering contains two parts: a real-valued non-random recurrence equation and the standard Kalman filtering.

  20. Enhancement of Spanish Oesophageal Speech vowels using coherent subband modulator Kalman filtering.

    Science.gov (United States)

    Ishaq, Rizwan; Zapirain, Begoña García

    2016-01-01

    This paper proposes an Oesophageal Speech (OES) enhancement method, based on Kalman filtering. The Kalman filter is applied to modulators of OES frequency subbands instead of the fullband signal. The OES frequency subbands are decomposed into modulators and carriers components using coherent demodulation. In comparison with fullband Kalman filtering and pole stabilization, the proposed technique shows better results. The system performance is evaluated objectively and subjectively using the Harmonic to Noise Ratio (HNR) and Mean Opinion Score (MOS) respectively. Results have shown that Kalman filter in subband modulators processing is robust and efficient, improving the HNR by 4 to 5 dB for all Spanish vowels.

  1. Using Kalman filtering to predict time-varying parameters in a model predicting baroreflex regulation during head-up tilt

    DEFF Research Database (Denmark)

    Matzuka, Brett; Mehlsen, Jesper; Tran, Hien

    2015-01-01

    , while the second uses the ensemble transform Kalman filter (ETKF) [1], [12], [13], [35]. In addition, we show that the delayed rejection adaptive Metropolis (DRAM) algorithm can be used for predicting parameter uncertainties within the spline methodology, which is compared with the variability obtained...

  2. Applying Kalman filtering to investigate tropospheric effects in VLBI

    Science.gov (United States)

    Soja, Benedikt; Nilsson, Tobias; Karbon, Maria; Heinkelmann, Robert; Liu, Li; Lu, Cuixian; Andres Mora-Diaz, Julian; Raposo-Pulido, Virginia; Xu, Minghui; Schuh, Harald

    2014-05-01

    Very Long Baseline Interferometry (VLBI) currently provides results, e.g., estimates of the tropospheric delays, with a delay of more than two weeks. In the future, with the coming VLBI2010 Global Observing System (VGOS) and increased usage of electronic data transfer, it is planned that the time between observations and results is decreased. This may, for instance, allow the integration of VLBI-derived tropospheric delays into numerical weather prediction models. Therefore, future VLBI analysis software packages need to be able to process the observational data autonomously in near real-time. For this purpose, we have extended the Vienna VLBI Software (VieVS) by a Kalman filter module. This presentation describes the filter and discusses its application for tropospheric studies. Instead of estimating zenith wet delays as piece-wise linear functions in a least-squares adjustment, the Kalman filter allows for more sophisticated stochastic modeling. We start with a random walk process to model the time-dependent behavior of the zenith wet delays. Other possible approaches include the stochastic model described by turbulence theory, e.g. the model by Treuhaft and Lanyi (1987). Different variance-covariance matrices of the prediction error, depending on the time of the year and the geographic latitude, have been tested. In winter and closer to the poles, lower variances and covariances are appropriate. The horizontal variations in tropospheric delays have been investigated by comparing three different strategies: assumption of a horizontally stratified troposphere, using north and south gradients modeled, e.g., as Gauss-Markov processes, and applying a turbulence model assuming correlations between observations in different azimuths. By conducting Monte-Carlo simulations of current standard VLBI networks and of future VGOS networks, the different tropospheric modeling strategies are investigated. For this purpose, we use the simulator module of VieVS which takes into

  3. 基于改进Kalman滤波算法的多模型融合建模方法%Multi-model fusion modeling method based on improved Kalman filtering algorithm

    Institute of Scientific and Technical Information of China (English)

    朱鹏飞; 夏陆岳; 潘海天

    2015-01-01

    A multi-model fusion modeling method based on improved Kalman filter algorithm was presented for soft sensor of key quality index or state variable in the polymerization process. First, a data-driven soft-sensor modeling method was proposed by combining mixtures of kernels principal component analysis (K2PCA) with artificial neural network (ANN). Second, a parallel hybrid model was constructed by fusing the data-driven model with a mechanism model through an improved Kalman filtering algorithm. Moreover, a linear smoothing filter and a model variance updating method were adopted for optimizing the hybrid model, which could enhance performance and improve prediction stability of the hybrid model. The application of the proposed multi-model fusion modeling method in the vinyl chloride polymerization rate prediction verified that the hybrid model was more effective in comparison with single-model cases (thermodynamic mechanism model or K2PCA-ANN model). The proposed multi-model fusion modeling method would provide basic conditions for control and optimization of PVC polymerization process in further research.%针对聚合物生产过程重要质量控制指标或状态变量的软测量问题,提出了一种基于改进Kalman滤波算法的多模型融合建模方法。将混合核函数主元分析(K2PCA)与人工神经网络(ANN)相结合,建立一种基于K2PCA-ANN的数据驱动模型;利用改进Kalman滤波算法实现K2PCA-ANN模型与机理模型融合,构建一种并联结构的混合模型;协调二次滤波(线性滑动平滑)和方差更新对混合模型进行优化处理,使混合模型的估计性能尽可能地达到最优,使混合模型的预测稳定性得到有效改善。将该多模型融合建模方法应用于氯乙烯聚合过程聚合速率软测量中,应用研究结果表明:与单一的机理模型或K2PCA-ANN数据驱动模型的预测性能相比,该建模方法建立的聚合速率模型具有更佳的

  4. Kalman filtering to suppress spurious signals in Adaptive Optics control

    Energy Technology Data Exchange (ETDEWEB)

    Poyneer, L; Veran, J P

    2010-03-29

    In many scenarios, an Adaptive Optics (AO) control system operates in the presence of temporally non-white noise. We use a Kalman filter with a state space formulation that allows suppression of this colored noise, hence improving residual error over the case where the noise is assumed to be white. We demonstrate the effectiveness of this new filter in the case of the estimated Gemini Planet Imager tip-tilt environment, where there are both common-path and non-common path vibrations. We discuss how this same framework can also be used to suppress spatial aliasing during predictive wavefront control assuming frozen flow in a low-order AO system without a spatially filtered wavefront sensor, and present experimental measurements from Altair that clearly reveal these aliased components.

  5. Kalman filtering to suppress spurious signals in Adaptive Optics control

    Energy Technology Data Exchange (ETDEWEB)

    Poyneer, L; Veran, J P

    2010-03-29

    In many scenarios, an Adaptive Optics (AO) control system operates in the presence of temporally non-white noise. We use a Kalman filter with a state space formulation that allows suppression of this colored noise, hence improving residual error over the case where the noise is assumed to be white. We demonstrate the effectiveness of this new filter in the case of the estimated Gemini Planet Imager tip-tilt environment, where there are both common-path and non-common path vibrations. We discuss how this same framework can also be used to suppress spatial aliasing during predictive wavefront control assuming frozen flow in a low-order AO system without a spatially filtered wavefront sensor, and present experimental measurements from Altair that clearly reveal these aliased components.

  6. Kalman Filter Estimation of Spinning Spacecraft Attitude using Markley Variables

    Science.gov (United States)

    Sedlak, Joseph E.; Harman, Richard

    2004-01-01

    There are several different ways to represent spacecraft attitude and its time rate of change. For spinning or momentum-biased spacecraft, one particular representation has been put forward as a superior parameterization for numerical integration. Markley has demonstrated that these new variables have fewer rapidly varying elements for spinning spacecraft than other commonly used representations and provide advantages when integrating the equations of motion. The current work demonstrates how a Kalman filter can be devised to estimate the attitude using these new variables. The seven Markley variables are subject to one constraint condition, making the error covariance matrix singular. The filter design presented here explicitly accounts for this constraint by using a six-component error state in the filter update step. The reduced dimension error state is unconstrained and its covariance matrix is nonsingular.

  7. Estimation of time varying system parameters from ambient response using improved Particle-Kalman filter with correlated noise

    Science.gov (United States)

    Sen, Subhamoy; Crinière, Antoine; Mevel, Laurent; Cerou, Frederic; Dumoulin, Jean

    2017-04-01

    Keywords: Parameter estimation; Kalman filter; Particle filter; Particle-Kalman filter; Correlated noise Although Kalman filter (KF) was originally proposed for system control i.e. steering a system as desired by monitoring the system states, its application for parameter estimation problems is widespread because of the excellent similarity between these two apparently different problem types in state space description. In standard Kalman filter, system dynamics is described through the dynamics of certain internal variable, termed as states, evolving over time as defined by an assumed process model, while a measurement model maps these states to measurements. In some parameter estimation problems, the system is replaced by a state space formulation of the dynamic model with parameters appended in the unobserved states and collectively observed through the response measurements. Filtering based parameter estimation problems are thus inherently nonlinear due to the required nonlinear mapping of parameters to the corresponding observations. Being a linear estimator, Kalman Filter (KF) cannot be employed for such nonlinear system estimation and alternative filtering algorithms (eg. Particle filter) are therefore generally used. However, being model based, these filters optimally estimate the parameters of a quasi-static model of the real dynamic system. Consequently, any time variation in the system dynamics may completely diverge the estimation yielding a false or infeasible solution. By decoupling the estimation of system states and parameters, and applying concurrent filtering strategy that attempts conditional estimation of states based on parameters and vice versa, time varying systems can be estimated. This article attempts to combine KF with Particle filter (PF) and apply them for estimation of states and system parameters respectively on a system with correlated noise in process and measurement. The idea is to nest a bank of linear KFs for state estimation

  8. Penalized Ensemble Kalman Filters for High Dimensional Non-linear Systems

    CERN Document Server

    Hou, Elizabeth; Hero, Alfred O

    2016-01-01

    The ensemble Kalman filter (EnKF) is a data assimilation technique that uses an ensemble of models, updated with data, to track the time evolution of a non-linear system. It does so by using an empirical approximation to the well-known Kalman filter. Unfortunately, its performance suffers when the ensemble size is smaller than the state space, as is often the case for computationally burdensome models. This scenario means that the empirical estimate of the state covariance is not full rank and possibly quite noisy. To solve this problem in this high dimensional regime, a computationally fast and easy to implement algorithm called the penalized ensemble Kalman filter (PEnKF) is proposed. Under certain conditions, it can be proved that the PEnKF does not require more ensemble members than state dimensions in order to have good performance. Further, the proposed approach does not require special knowledge of the system such as is used by localization methods. These theoretical results are supported with superior...

  9. Achieving comparable uncertainty estimates with Kalman filters or linear smoothers for bathymetry data

    Science.gov (United States)

    Bourgeois, Brian S.; Elmore, Paul A.; Avera, William E.; Zambo, Samantha J.

    2016-07-01

    This paper examines and contrasts two estimation methods, Kalman filtering and linear smoothing, for creating interpolated data products from bathymetry measurements. Using targeted examples, we demonstrate previously obscured behavior showing the dependence of linear smoothers on the spatial arrangement of the measurements, yielding markedly different estimation results than the Kalman filter. For bathymetry data, we have modified the variance estimates from both the Kalman filter and linear smoothers to obtain comparable estimators for dense data. These comparable estimators produce uncertainty estimates that have statistically insignificant differences via hypothesis testing. Achieving comparable estimation is accomplished by applying the "propagated uncertainty" concept and a numerical realization of Tobler's principle to the measurement data prior to the computation of the estimate. We show new mathematical derivations for these modifications. In addition, we show test results with (a) synthetic data and (b) gridded bathymetry in the area of the Scripps and La Jolla Canyons. Our tenfold cross-validation for case (b) shows that the modified equations create comparable uncertainty for both gridding algorithms with null hypothesis acceptance rates of greater than 99.95% of the data points. In contrast, bilinear interpolation has 10 times the amount of rejection. We then discuss how the uncertainty estimators are, in principle, applicable to interpolate geophysical data other than bathymetry.

  10. Consensus+Innovations Distributed Kalman Filter With Optimized Gains

    Science.gov (United States)

    Das, Subhro; Moura, Jose M. F.

    2017-01-01

    In this paper, we address the distributed filtering and prediction of time-varying random fields represented by linear time-invariant (LTI) dynamical systems. The field is observed by a sparsely connected network of agents/sensors collaborating among themselves. We develop a Kalman filter type consensus+innovations distributed linear estimator of the dynamic field termed as Consensus+Innovations Kalman Filter. We analyze the convergence properties of this distributed estimator. We prove that the mean-squared error of the estimator asymptotically converges if the degree of instability of the field dynamics is within a pre-specified threshold defined as tracking capacity of the estimator. The tracking capacity is a function of the local observation models and the agent communication network. We design the optimal consensus and innovation gain matrices yielding distributed estimates with minimized mean-squared error. Through numerical evaluations, we show that, the distributed estimator with optimal gains converges faster and with approximately 3dB better mean-squared error performance than previous distributed estimators.

  11. An Extended Kalman Filter with a Computed Mean Square Error Bound

    OpenAIRE

    Hexner, Gyorgy; Weiss, Haim

    2014-01-01

    The paper proposes a new recursive filter for non-linear systems that inherently computes a valid bound on the mean square estimation error. The proposed filter, bound based extended Kalman, (BEKF) is in the form of an extended Kalman filter. The main difference of the proposed filter from the conventional extended Kalman filter is in the use of a computed mean square error bound matrix, to calculate the filter gain, and to serve as bound on the actual mean square error. The paper shows that ...

  12. Application of Kalman Filter on modelling interest rates

    Directory of Open Access Journals (Sweden)

    Long H. Vo

    2014-03-01

    Full Text Available This study aims to test the feasibility of using a data set of 90-day bank bill forward rates from the Australian market to predict spot interest rates. To achieve this goal I utilized the application of Kalman Filter in a state space model with time-varying state variable. It is documented that in the case of short-term interest rates,the state space model yields robust predictive power. In addition, this predictive power of implied forward rate is heavily impacted by the existence of a time-varying risk premium in the term structure.

  13. 4-D-Var or ensemble Kalman filter?

    OpenAIRE

    Kalnay, Eugenia; LI, HONG; Miyoshi, Takemasa; Yang, Shu-Chih; Ballabrera-Poy, Joaquim

    2007-01-01

    We consider the relative advantages of two advanced data assimilation systems, 4-D-Var and ensemble Kalman filter (EnKF), currently in use or under consideration for operational implementation. With the Lorenz model, we explore the impact of tuning assimilation parameters such as the assimilation window length and background error covariance in 4-D-Var, variance inflation in EnKF, and the effect of model errors and reduced observation coverage. For short assimilation windows EnKF gives more a...

  14. Adaptive training of feedforward neural networks by Kalman filtering

    Energy Technology Data Exchange (ETDEWEB)

    Ciftcioglu, Oe. [Istanbul Technical Univ. (Turkey). Dept. of Electrical Engineering; Tuerkcan, E. [Netherlands Energy Research Foundation (ECN), Petten (Netherlands)

    1995-02-01

    Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.).

  15. Optimal subband Kalman filter for normal and oesophageal speech enhancement.

    Science.gov (United States)

    Ishaq, Rizwan; García Zapirain, Begoña

    2014-01-01

    This paper presents the single channel speech enhancement system using subband Kalman filtering by estimating optimal Autoregressive (AR) coefficients and variance for speech and noise, using Weighted Linear Prediction (WLP) and Noise Weighting Function (NWF). The system is applied for normal and Oesophageal speech signals. The method is evaluated by Perceptual Evaluation of Speech Quality (PESQ) score and Signal to Noise Ratio (SNR) improvement for normal speech and Harmonic to Noise Ratio (HNR) for Oesophageal Speech (OES). Compared with previous systems, the normal speech indicates 30% increase in PESQ score, 4 dB SNR improvement and OES shows 3 dB HNR improvement.

  16. Telescope Multi-Field Wavefront Control with a Kalman Filter

    Science.gov (United States)

    Lou, John Z.; Redding, David; Sigrist, Norbert; Basinger, Scott

    2008-01-01

    An effective multi-field wavefront control (WFC) approach is demonstrated for an actuated, segmented space telescope using wavefront measurements at the exit pupil, and the optical and computational implications of this approach are discussed. The integration of a Kalman Filter as an optical state estimator into the wavefront control process to further improve the robustness of the optical alignment of the telescope will also be discussed. Through a comparison of WFC performances between on-orbit and ground-test optical system configurations, the connection (and a possible disconnection) between WFC and optical system alignment under these circumstances are analyzed. Our MACOS-based computer simulation results will be presented and discussed.

  17. Application of Unscented Kalman Filter in Satellite Orbit Simulation

    Institute of Scientific and Technical Information of China (English)

    ZHAO Dongming; CAI Zhiwu

    2006-01-01

    A new estimate method is proposed, which takes advantage of the unscented transform method, thus the true mean and covariance are approximated more accurately. The new method can be applied to non-linear systems without the linearization process necessary for the EKF, and it does not demand a Gaussian distribution of noise and what's more, its ease of implementation and more accurate estimation features enables it to demonstrate its good performance in the experiment of satellite orbit simulation. Numerical experiments show that the application of the unscented Kalman filter is more effective than the EKF.

  18. Comparison of Sigma-Point and Extended Kalman Filters on a Realistic Orbit Determination Scenario

    Science.gov (United States)

    Gaebler, John; Hur-Diaz. Sun; Carpenter, Russell

    2010-01-01

    Sigma-point filters have received a lot of attention in recent years as a better alternative to extended Kalman filters for highly nonlinear problems. In this paper, we compare the performance of the additive divided difference sigma-point filter to the extended Kalman filter when applied to orbit determination of a realistic operational scenario based on the Interstellar Boundary Explorer mission. For the scenario studied, both filters provided equivalent results. The performance of each is discussed in detail.

  19. Comparison of Sigma-Point and Extended Kalman Filters on a Realistic Orbit Determination Scenario

    Science.gov (United States)

    Gaebler, John; Hur-Diaz. Sun; Carpenter, Russell

    2010-01-01

    Sigma-point filters have received a lot of attention in recent years as a better alternative to extended Kalman filters for highly nonlinear problems. In this paper, we compare the performance of the additive divided difference sigma-point filter to the extended Kalman filter when applied to orbit determination of a realistic operational scenario based on the Interstellar Boundary Explorer mission. For the scenario studied, both filters provided equivalent results. The performance of each is discussed in detail.

  20. A novel strong tracking finite-difference extended Kalman filter for nonlinear eye tracking

    Institute of Scientific and Technical Information of China (English)

    ZHANG ZuTao; ZHANG JiaShu

    2009-01-01

    Non-Intrusive methods for eye tracking are Important for many applications of vision-based human computer interaction. However, due to the high nonlinearity of eye motion, how to ensure the robust-ness of external interference and accuracy of eye tracking poses the primary obstacle to the integration of eye movements into today's interfaces. In this paper, we present a strong tracking finite-difference extended Kalman filter algorithm, aiming to overcome the difficulty In modeling nonlinear eye tracking. In filtering calculation, strong tracking factor is introduced to modify a priori covariance matrix and im-prove the accuracy of the filter. The filter uses finite-difference method to calculate partial derivatives of nonlinear functions for eye tracking. The latest experimental results show the validity of our method for eye tracking under realistic conditions.

  1. Interaction of Lyapunov vectors in the formulation of the nonlinear extension of the Kalman filter.

    Science.gov (United States)

    Palatella, Luigi; Trevisan, Anna

    2015-04-01

    When applied to strongly nonlinear chaotic dynamics the extended Kalman filter (EKF) is prone to divergence due to the difficulty of correctly forecasting the forecast error probability density function. In operational forecasting applications ensemble Kalman filters circumvent this problem with empirical procedures such as covariance inflation. This paper presents an extension of the EKF that includes nonlinear terms in the evolution of the forecast error estimate. This is achieved starting from a particular square-root implementation of the EKF with assimilation confined in the unstable subspace (EKF-AUS), that is, the span of the Lyapunov vectors with non-negative exponents. When the error evolution is nonlinear, the space where it is confined is no more restricted to the unstable and neutral subspace causing filter divergence. The algorithm presented here, denominated EKF-AUS-NL, includes the nonlinear terms in the error dynamics: These result from the nonlinear interaction among the leading Lyapunov vectors and account for all directions where the error growth may take place. Numerical results show that with the nonlinear terms included, filter divergence can be avoided. We test the algorithm on the Lorenz96 model, showing very promising results.

  2. Estimation of FBMC/OQAM fading channels using dual Kalman filters.

    Science.gov (United States)

    Aldababseh, Mahmoud; Jamoos, Ali

    2014-01-01

    We address the problem of estimating time-varying fading channels in filter bank multicarrier (FBMC/OQAM) wireless systems based on pilot symbols. The standard solution to this problem is the least square (LS) estimator or the minimum mean square error (MMSE) estimator with possible adaptive implementation using recursive least square (RLS) algorithm or least mean square (LMS) algorithm. However, these adaptive filters cannot well-exploit fading channel statistics. To take advantage of fading channel statistics, the time evolution of the fading channel is modeled by an autoregressive process and tracked by Kalman filter. Nevertheless, this requires the autoregressive parameters which are usually unknown. Thus, we propose to jointly estimate the FBMC/OQAM fading channels and their autoregressive parameters based on dual optimal Kalman filters. Once the fading channel coefficients at pilot symbol positions are estimated by the proposed method, the fading channel coefficients at data symbol positions are then estimated by using some interpolation methods such as linear, spline, or low-pass interpolation. The comparative simulation study we carried out with existing techniques confirms the effectiveness of the proposed method.

  3. Estimation of FBMC/OQAM Fading Channels Using Dual Kalman Filters

    Directory of Open Access Journals (Sweden)

    Mahmoud Aldababseh

    2014-01-01

    Full Text Available We address the problem of estimating time-varying fading channels in filter bank multicarrier (FBMC/OQAM wireless systems based on pilot symbols. The standard solution to this problem is the least square (LS estimator or the minimum mean square error (MMSE estimator with possible adaptive implementation using recursive least square (RLS algorithm or least mean square (LMS algorithm. However, these adaptive filters cannot well-exploit fading channel statistics. To take advantage of fading channel statistics, the time evolution of the fading channel is modeled by an autoregressive process and tracked by Kalman filter. Nevertheless, this requires the autoregressive parameters which are usually unknown. Thus, we propose to jointly estimate the FBMC/OQAM fading channels and their autoregressive parameters based on dual optimal Kalman filters. Once the fading channel coefficients at pilot symbol positions are estimated by the proposed method, the fading channel coefficients at data symbol positions are then estimated by using some interpolation methods such as linear, spline, or low-pass interpolation. The comparative simulation study we carried out with existing techniques confirms the effectiveness of the proposed method.

  4. COMPARATIVE STUDY OF DIFFERENT KALMAN FILTER IMPLEMENTATIONS IN POWER SYSTEM STABILITY

    Directory of Open Access Journals (Sweden)

    H. H. Goh

    2014-01-01

    Full Text Available Voltage stability and voltage collapse issues have in recent years begun to constitute an unpleasant warning to the operational security of power systems. Many techniques have been investigated in order to predict the point of voltage collapse. However, there are still several restrictions due to the insufficiency of current system state information. Accompanied by the commencement of the Phasor Measurement Units (PMUs evolving technology, it donates a solution to enhance the existing power system state estimation. In consequence, the significances to develop preferable methods that would provide a preliminary warning before the voltage collapse had grabbed the attention. This study covers the forming of real-time system monitoring methods that able to provide a timely warning in the power system. The algorithms used to estimate the points of collapse are according to the theory that voltage instability is approximately linked to the maximum load ability of a transmission network. As a result, the critical operating conditions (peak of maximum deliverable power come when the system Thevenin impedance is equal to the load impedance. This study focuses specifically on research about the motivation and the application of different Kalman filter implementations such as Discrete Kalman Filter (DKF, Extended Kalman Filter (EKF and Unscented Kalman Filter (UKF are used to track the Thevenin parameters. Therefore, the implications of this research paper are to determine the robustness and reliability of the proposed tracking methods. As compared to previous studies, the tracking process is just mainly focused on DKF method only, while the novelty throughout this study is to compare the performances and efficiencies of different Kalman filters in determining the maximum load ability on the 2 different types of test systems. Accompanying, the parameters are utilized in real-time voltage instability estimator to discover the current system’s condition

  5. A chaotic communication system of improved performance based on the Derivative-free nonlinear Kalman filter

    Science.gov (United States)

    Rigatos, Gerasimos

    2016-07-01

    The Derivative-free nonlinear Kalman Filter is used for developing a communication system that is based on a chaotic modulator such as the Duffing system. In the transmitter's side, the source of information undergoes modulation (encryption) in which a chaotic signal generated by the Duffing system is the carrier. The modulated signal is transmitted through a communication channel and at the receiver's side demodulation takes place, after exploiting the estimation provided about the state vector of the chaotic oscillator by the Derivative-free nonlinear Kalman Filter. Evaluation tests confirm that the proposed filtering method has improved performance over the Extended Kalman Filter and reduces significantly the rate of transmission errors. Moreover, it is shown that the proposed Derivative-free nonlinear Kalman Filter can work within a dual Kalman Filtering scheme, for performing simultaneously transmitter-receiver synchronisation and estimation of unknown coefficients of the communication channel.

  6. Multisensor Distributed Track Fusion AlgorithmBased on Strong Tracking Filter and Feedback Integration1)

    Institute of Scientific and Technical Information of China (English)

    YANGGuo-Sheng; WENCheng-Lin; TANMin

    2004-01-01

    A new multisensor distributed track fusion algorithm is put forward based on combiningthe feedback integration with the strong tracking Kalman filter. Firstly, an effective tracking gateis constructed by taking the intersection of the tracking gates formed before and after feedback.Secondly, on the basis of the constructed effective tracking gate, probabilistic data association andstrong tracking Kalman filter are combined to form the new multisensor distributed track fusionalgorithm. At last, simulation is performed on the original algorithm and the algorithm presented.

  7. Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time

    KAUST Repository

    Kelly, D. T B

    2014-09-22

    The ensemble Kalman filter (EnKF) is a method for combining a dynamical model with data in a sequential fashion. Despite its widespread use, there has been little analysis of its theoretical properties. Many of the algorithmic innovations associated with the filter, which are required to make a useable algorithm in practice, are derived in an ad hoc fashion. The aim of this paper is to initiate the development of a systematic analysis of the EnKF, in particular to do so for small ensemble size. The perspective is to view the method as a state estimator, and not as an algorithm which approximates the true filtering distribution. The perturbed observation version of the algorithm is studied, without and with variance inflation. Without variance inflation well-posedness of the filter is established; with variance inflation accuracy of the filter, with respect to the true signal underlying the data, is established. The algorithm is considered in discrete time, and also for a continuous time limit arising when observations are frequent and subject to large noise. The underlying dynamical model, and assumptions about it, is sufficiently general to include the Lorenz \\'63 and \\'96 models, together with the incompressible Navier-Stokes equation on a two-dimensional torus. The analysis is limited to the case of complete observation of the signal with additive white noise. Numerical results are presented for the Navier-Stokes equation on a two-dimensional torus for both complete and partial observations of the signal with additive white noise.

  8. 一种基于Kalman滤波的双轮机器人姿态控制算法%A Kind of Attitude Control Method of Two-Wheel Robot Based on Kalman Filtering Algorithm

    Institute of Scientific and Technical Information of China (English)

    任国华; 唐昌进; 蒋刚

    2013-01-01

    采用自制的双轮式结构机器人作为试验平台,以ADXL345三轴加速度计和ITG3200三轴陀螺仪微重力MEMS微机械电子技术的传感器作为机器人的角度传感器,通过建立离散卡尔曼滤波器数学模型对两种传感器的数据进行滤波融合,使其得到更稳定更准确更接近真实值的角度值,使用STM32F 103VE获取动态的角度信息,通过变参数自整定平衡控制算法的调节使机器人保持平衡,实验结果验证了该方法的可行性,对特殊环境自动无人巡航机器人和载人巡航机器人控制有很大的研究价值.%A self-made two-wheeled structure robot was chosen as an experiment platform.Using ADXL345 three-axis accelerometer and ITG3200 axis gyroscope microgravity MEMS micro-mechanical electronic technology sensors as the angle of the robot sensor, through the establishment of discrete kalman filler mathematical model of two kinds of sensor data filtering fusion, more stable, more accurate and closer to the real value of the Angle value was got. TM32F103VET6 was used to get dynamic information. Through the variable parameter self-setting balance control algorithm of regulation, robot was made to keep the balance. Experimental results verified the feasibility of this method, providing great research value for special environment automatic unmanned cruising robot and manned cruising robot control.

  9. Non-linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter

    DEFF Research Database (Denmark)

    Andreasen, Martin Møller

    This paper shows how non-linear DSGE models with potential non-normal shocks can be estimated by Quasi-Maximum Likelihood based on the Central Difference Kalman Filter (CDKF). The advantage of this estimator is that evaluating the quasi log-likelihood function only takes a fraction of a second. T...

  10. A Tensor Network Kalman filter with an application in recursive MIMO Volterra system identification

    OpenAIRE

    Batselier, Kim; Chen, Zhongming; Wong, Ngai

    2016-01-01

    This article introduces a Tensor Network Kalman filter, which can estimate state vectors that are exponentially large without ever having to explicitly construct them. The Tensor Network Kalman filter also easily accommodates the case where several different state vectors need to be estimated simultaneously. The key lies in rewriting the standard Kalman equations as tensor equations and then implementing them using Tensor Networks, which effectively transforms the exponential storage cost and...

  11. An Adaptive Channel Interpolator Based on Kalman Filter for LTE Uplink in High Doppler Spread Environments

    Directory of Open Access Journals (Sweden)

    Arslan Hüseyin

    2009-01-01

    Full Text Available Long-Term Evolution (LTE systems will employ single carrier frequency division multiple access (SC-FDMA for the uplink. Similar to the Orthogonal frequency-division multiple access (OFDMA technology, SC-FDMA is sensitive to frequency offsets leading to intercarrier interference (ICI. In this paper, we propose a Kalman filter-based approach in order to mitigate ICI under high Doppler spread scenarios by tracking the variation of channel taps jointly in time domain for LTE uplink systems. Upon acquiring the estimates of channel taps from the Kalman tracker, we employ an interpolation algorithm based on polynomial fitting whose order is changed adaptively. The proposed method is evaluated under four different scenarios with different settings in order to reflect the impact of various critical parameters on the performance such as propagation environment, speed, and size of resource block (RB assignments. Results are given along with discussions.

  12. Design and implementation of Kalman filter based on FPGA%基于FPGA的Kalman滤波器实现研究

    Institute of Scientific and Technical Information of China (English)

    赵大建; 赵伟; 张兆亮; 宋国安; 刘建业

    2012-01-01

    卡尔曼(Kalman)滤波计算精度和速度是工程应用中是否成功的决定性条件,为进一步提高Kalman滤波算法在更复杂的环境下使用的性能,并能够同时满足实时性和精度的要求,采用现场可编程逻辑阵列(FPGA)技术,设计了Kalman滤波算法在FPGA上的实现方案,选择了一种可以同时满足精度和实时性的方案进行实现,对算法中的矩阵相乘、状态机的应用以及资源分时复用等关键技术进行了设计.通过与Matlab及DSP的计算结果相对比,验证了在FPGA内实现Kalman滤波器的优势.%The calculation precision and speed of Kalman filtering are the key factors in application. Aiming to improve the performance of Kalman filtering algorithm applied to a more complex condition and meet the requirements of high accuracy and real-time performance, a scheme for implementing the Kalman filtering algorithm was designed with FPGA technology. Some key techniques such as the multiplication of matrixes, the application of finite state machine and the same source usage at different time were designed. The superiority of implementing the Kalman filter in FPGA was validated by comparing the verified results of Kalman filter implemented in FPGA with the calculated results of Matlab and DSP respectively.

  13. Skew redundant MEMS IMU calibration using a Kalman filter

    Science.gov (United States)

    Jafari, M.; Sahebjameyan, M.; Moshiri, B.; Najafabadi, T. A.

    2015-10-01

    In this paper, a novel calibration procedure for skew redundant inertial measurement units (SRIMUs) based on micro-electro mechanical systems (MEMS) is proposed. A general model of the SRIMU measurements is derived which contains the effects of bias, scale factor error and misalignments. For more accuracy, the effect of lever arms of the accelerometers to the center of the table are modeled and compensated in the calibration procedure. Two separate Kalman filters (KFs) are proposed to perform the estimation of error parameters for gyroscopes and accelerometers. The predictive error minimization (PEM) stochastic modeling method is used to simultaneously model the effect of bias instability and random walk noise on the calibration Kalman filters to diminish the biased estimations. The proposed procedure is simulated numerically and has expected experimental results. The calibration maneuvers are applied using a two-axis angle turntable in a way that the persistency of excitation (PE) condition for parameter estimation is met. For this purpose, a trapezoidal calibration profile is utilized to excite different deterministic error parameters of the accelerometers and a pulse profile is used for the gyroscopes. Furthermore, to evaluate the performance of the proposed KF calibration method, a conventional least squares (LS) calibration procedure is derived for the SRIMUs and the simulation and experimental results compare the functionality of the two proposed methods with each other.

  14. Reconstruction of spacecraft rotational motion using a Kalman filter

    Science.gov (United States)

    Pankratov, V. A.; Sazonov, V. V.

    2016-05-01

    Quasi-static microaccelerations of four satellites of the Foton series (nos. 11, 12, M-2, M-3) were monitored as follows. First, according to measurements of onboard sensors obtained in a certain time interval, spacecraft rotational motion was reconstructed in this interval. Then, along the found motion, microacceleration at a given onboard point was calculated according to the known formula as a function of time. The motion was reconstructed by the least squares method using the solutions to the equations of satellite rotational motion. The time intervals in which these equations make reconstruction possible were from one to five orbital revolutions. This length is increased with the modulus of the satellite angular velocity. To get an idea on microaccelerations and satellite motion during an entire flight, the motion was reconstructed in several tens of such intervals. This paper proposes a method for motion reconstruction suitable for an interval of arbitrary length. The method is based on the Kalman filter. We preliminary describe a new version of the method for reconstructing uncontrolled satellite rotational motion from magnetic measurements using the least squares method, which is essentially used to construct the Kalman filter. The results of comparison of both methods are presented using the data obtained on a flight of the Foton M-3.

  15. Method for Improving Indoor Positioning Accuracy Using Extended Kalman Filter

    Directory of Open Access Journals (Sweden)

    Seoung-Hyeon Lee

    2016-01-01

    Full Text Available Beacons using bluetooth low-energy (BLE technology have emerged as a new paradigm of indoor positioning service (IPS because of their advantages such as low power consumption, miniaturization, wide signal range, and low cost. However, the beacon performance is poor in terms of the indoor positioning accuracy because of noise, motion, and fading, all of which are characteristics of a bluetooth signal and depend on the installation location. Therefore, it is necessary to improve the accuracy of beacon-based indoor positioning technology by fusing it with existing indoor positioning technology, which uses Wi-Fi, ZigBee, and so forth. This study proposes a beacon-based indoor positioning method using an extended Kalman filter that recursively processes input data including noise. After defining the movement of a smartphone on a flat two-dimensional surface, it was assumed that the beacon signal is nonlinear. Then, the standard deviation and properties of the beacon signal were analyzed. According to the analysis results, an extended Kalman filter was designed and the accuracy of the smartphone’s indoor position was analyzed through simulations and tests. The proposed technique achieved good indoor positioning accuracy, with errors of 0.26 m and 0.28 m from the average x- and y-coordinates, respectively, based solely on the beacon signal.

  16. 3D hand tracking using Kalman filter in depth space

    Science.gov (United States)

    Park, Sangheon; Yu, Sunjin; Kim, Joongrock; Kim, Sungjin; Lee, Sangyoun

    2012-12-01

    Hand gestures are an important type of natural language used in many research areas such as human-computer interaction and computer vision. Hand gestures recognition requires the prior determination of the hand position through detection and tracking. One of the most efficient strategies for hand tracking is to use 2D visual information such as color and shape. However, visual-sensor-based hand tracking methods are very sensitive when tracking is performed under variable light conditions. Also, as hand movements are made in 3D space, the recognition performance of hand gestures using 2D information is inherently limited. In this article, we propose a novel real-time 3D hand tracking method in depth space using a 3D depth sensor and employing Kalman filter. We detect hand candidates using motion clusters and predefined wave motion, and track hand locations using Kalman filter. To verify the effectiveness of the proposed method, we compare the performance of the proposed method with the visual-based method. Experimental results show that the performance of the proposed method out performs visual-based method.

  17. Multimodal Degradation Prognostics Based on Switching Kalman Filter Ensemble.

    Science.gov (United States)

    Lim, Pin; Goh, Chi Keong; Tan, Kay Chen; Dutta, Partha

    2017-01-01

    For accurate prognostics, users have to determine the current health of the system and predict future degradation pattern of the system. An increasingly popular approach toward tackling prognostic problems involves the use of switching models to represent various degradation phases, which the system undergoes. Such approaches have the advantage of determining the exact degradation phase of the system and being able to handle nonlinear degradation models through piecewise linear approximation. However, limitations of such existing methods include, limited applicability due to the discretization of predicted remaining useful life, insufficient robustness due to the use of single models and others. This paper circumvents these limitations by proposing a hybrid of ensemble methods with switching methods. The proposed method first implements a switching Kalman filter (SKF) to classify between various linear degradation phases, then predict the future propagation of fault dimension using appropriate Kalman filters for each phase. This proposed method achieves both continuous and discrete prediction values representing the remaining life and degradation phase of the system, respectively. The proposed framework is shown via a case study on benchmark simulated aeroengine data sets. The evaluation of the proposed framework shows that the proposed method achieves better accuracy and robustness against noise compared with other methods reported in the literature. The results also indicate the effectiveness of the SKF in detecting the switching point between various degradation modes.

  18. Tractography from HARDI using an intrinsic unscented Kalman filter.

    Science.gov (United States)

    Cheng, Guang; Salehian, Hesamoddin; Forder, John R; Vemuri, Baba C

    2015-01-01

    A novel adaptation of the unscented Kalman filter (UKF) was recently introduced in literature for simultaneous multitensor estimation and fiber tractography from diffusion MRI. This technique has the advantage over other tractography methods in terms of computational efficiency, due to the fact that the UKF simultaneously estimates the diffusion tensors and propagates the most consistent direction to track along. This UKF and its variants reported later in literature however are not intrinsic to the space of diffusion tensors. Lack of this key property can possibly lead to inaccuracies in the multitensor estimation as well as in the tractography. In this paper, we propose a novel intrinsic unscented Kalman filter (IUKF) in the space of diffusion tensors which are symmetric positive definite matrices, that can be used for simultaneous recursive estimation of multitensors and propagation of directional information for use in fiber tractography from diffusion weighted MR data. In addition to being more accurate, IUKF retains all the advantages of UKF mentioned above. We demonstrate the accuracy and effectiveness of the proposed method via experiments publicly available phantom data from the fiber cup-challenge (MICCAI 2009) and diffusion weighted MR scans acquired from human brains and rat spinal cords.

  19. ERP Estimation using a Kalman Filter in VLBI

    Science.gov (United States)

    Karbon, M.; Soja, B.; Nilsson, T.; Heinkelmann, R.; Liu, L.; Lu, C.; Mora-Diaz, J. A.; Raposo-Pulido, V.; Xu, M.; Schuh, H.

    2014-12-01

    Geodetic Very Long Baseline Interferometry (VLBI) is one of the primary space geodetic techniques, providing the full set of Earth Orientation Parameters (EOP), and it is unique for observing long term Universal Time (UT1). For applications such as satellite-based navigation and positioning, accurate and continuous ERP obtained in near real-time are essential. They also allow the precise tracking of interplanetary spacecraft. One of the goals of VGOS (VLBI Global Observing System) is to provide such near real-time ERP. With the launch of this next generation VLBI system, the International VLBI Service for Geodesy and Astrometry (IVS) increased its efforts not only to reach 1 mm accuracy on a global scale but also to reduce the time span between the collection of VLBI observations and the availability of the final results substantially. Project VLBI-ART contributes to these objectives by implementing an elaborate Kalman filter, which represents a perfect tool for analyzing VLBI data in quasi real-time. The goal is to implement it in the GFZ version of the Vienna VLBI Software (VieVS) as a completely automated tool, i.e., with no need for human interaction. Here we present the methodology and first results of Kalman filtered EOP from VLBI data.

  20. Distributed Extended Kalman Filter for Position, Velocity, Time, Estimation in Satellite Navigation Receivers

    Directory of Open Access Journals (Sweden)

    O. Jakubov

    2013-09-01

    Full Text Available Common techniques for position-velocity-time estimation in satellite navigation, iterative least squares and the extended Kalman filter, involve matrix operations. The matrix inversion and inclusion of a matrix library pose requirements on a computational power and operating platform of the navigation processor. In this paper, we introduce a novel distributed algorithm suitable for implementation in simple parallel processing units each for a tracked satellite. Such a unit performs only scalar sum, subtraction, multiplication, and division. The algorithm can be efficiently implemented in hardware logic. Given the fast position-velocity-time estimator, frequent estimates can foster dynamic performance of a vector tracking receiver. The algorithm has been designed from a factor graph representing the extended Kalman filter by splitting vector nodes into scalar ones resulting in a cyclic graph with few iterations needed. Monte Carlo simulations have been conducted to investigate convergence and accuracy. Simulation case studies for a vector tracking architecture and experimental measurements with a real-time software receiver developed at CTU in Prague were conducted. The algorithm offers compromises in stability, accuracy, and complexity depending on the number of iterations. In scenarios with a large number of tracked satellites, it can outperform the traditional methods at low complexity.

  1. Reverse Engineering Sparse Gene Regulatory Networks Using Cubature Kalman Filter and Compressed Sensing

    Directory of Open Access Journals (Sweden)

    Amina Noor

    2013-01-01

    Full Text Available This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF and Kalman filter (KF techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cramér-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability.

  2. Detecting land cover change by evaluating the internal covariance matrix of the extended Kalman filter

    CSIR Research Space (South Africa)

    Salmon, BP

    2012-07-01

    Full Text Available In this paper, the internal operations of an Extended Kalman Filter is investigated to see if any useful information can be derived to detect land cover change in a MODIS time series. The Extended Kalman Filter expands its internal covariance if a...

  3. Sensorless Control of Electric Motors with Kalman Filters: Applications to Robotic and Industrial Systems

    Directory of Open Access Journals (Sweden)

    Gerasimos G. Rigatos

    2011-12-01

    Full Text Available The paper studies sensorless control for DC and induction motors, using Kalman Filtering techniques. First the case of a DC motor is considered and Kalman Filter-based control is implemented. Next the nonlinear model of a field-oriented induction motor is examined and the motor

  4. Application of Kalman Filtering Techniques for Microseismic Event Detection

    Science.gov (United States)

    Baziw, E.; Weir-Jones, I.

    - Microseismic monitoring systems are generally installed in areas of induced seismicity caused by human activity. Induced seismicity results from changes in the state of stress which may occur as a result of excavation within the rock mass in mining (i.e., rockbursts), and changes in hydrostatic pressures and rock temperatures (e.g., during fluid injection or extraction) in oil exploitation, dam construction or fluid disposal. Microseismic monitoring systems determine event locations and important source parameters such as attenuation, seismic moment, source radius, static stress drop, peak particle velocity and seismic energy. An essential part of the operation of a microseismic monitoring system is the reliable detection of microseismic events. In the absence of reliable, automated picking techniques, operators rely upon manual picking. This is time-consuming, costly and, in the presence of background noise, very prone to error. The techniques described in this paper not only permit the reliable identification of events in cluttered signal environments they have also enabled the authors to develop reliable automated event picking procedures. This opens the way to use microseismic monitoring as a cost-effective production/operations procedure. It has been the experience of the authors that in certain noisy environments, the seismic monitoring system may trigger on and subsequently acquire substantial quantities of erroneous data, due to the high energy content of the ambient noise. Digital filtering techniques need to be applied on the microseismic data so that the ambient noise is removed and event detection simplified. The monitoring of seismic acoustic emissions is a continuous, real-time process and it is desirable to implement digital filters which can also be designed in the time domain and in real-time such as the Kalman Filter. This paper presents a real-time Kalman Filter which removes the statistically describable background noise from the recorded

  5. Identifying Bearing Rotordynamic Coefficients using an Extended Kalman Filter

    Science.gov (United States)

    Miller, Bard A.; Howard, Samuel A.

    2008-01-01

    An Extended Kalman Filter is developed to estimate the linearized direct and indirect stiffness and damping force coefficients for bearings in rotor-dynamic applications from noisy measurements of the shaft displacement in response to imbalance and impact excitation. The bearing properties are modeled as stochastic random variables using a Gauss-Markov model. Noise terms are introduced into the system model to account for all of the estimation error, including modeling errors and uncertainties and the propagation of measurement errors into the parameter estimates. The system model contains two user-defined parameters that can be tuned to improve the filter s performance; these parameters correspond to the covariance of the system and measurement noise variables. The filter is also strongly influenced by the initial values of the states and the error covariance matrix. The filter is demonstrated using numerically simulated data for a rotor-bearing system with two identical bearings, which reduces the number of unknown linear dynamic coefficients to eight. The filter estimates for the direct damping coefficients and all four stiffness coefficients correlated well with actual values, whereas the estimates for the cross-coupled damping coefficients were the least accurate.

  6. Identifying Bearing Rotodynamic Coefficients Using an Extended Kalman Filter

    Science.gov (United States)

    Miller, Brad A.; Howard, Samuel A.

    2008-01-01

    An Extended Kalman Filter is developed to estimate the linearized direct and indirect stiffness and damping force coefficients for bearings in rotor dynamic applications from noisy measurements of the shaft displacement in response to imbalance and impact excitation. The bearing properties are modeled as stochastic random variables using a Gauss-Markov model. Noise terms are introduced into the system model to account for all of the estimation error, including modeling errors and uncertainties and the propagation of measurement errors into the parameter estimates. The system model contains two user-defined parameters that can be tuned to improve the filter's performance; these parameters correspond to the covariance of the system and measurement noise variables. The filter is also strongly influenced by the initial values of the states and the error covariance matrix. The filter is demonstrated using numerically simulated data for a rotor bearing system with two identical bearings, which reduces the number of unknown linear dynamic coefficients to eight. The filter estimates for the direct damping coefficients and all four stiffness coefficients correlated well with actual values, whereas the estimates for the cross-coupled damping coefficients were the least accurate.

  7. Short-term Forecasting Models on Occurrence of Rice Leaf Roller Based on Kalman Filter Algorithm%基于卡尔曼滤波算法的稻纵卷叶螟短期预测模型

    Institute of Scientific and Technical Information of China (English)

    包云轩; 陈心怡; 谢晓金; 王琳; 陆明红

    2016-01-01

    利用1994-2014年中国南方四大稻区(华南、西南、江岭和江淮稻区)代表性病虫测报站的稻纵卷叶螟逐候田间赶蛾量资料,筛选出影响各站稻纵卷叶螟发生量的关键气象因子,应用卡尔曼滤波方法分别对各站建立稻纵卷叶螟迁入期候发生量的卡尔曼短期预测模型,并计算模型的准确率、误差大小和稳定性。结果表明:(1)稻纵卷叶螟发生量与前一候和前两候的田间蛾量呈极显著正相关(P<0.01),与前一候的近地面最低气温、平均气温和最高气温呈极显著正相关(P<0.01),与前一候的地面气压呈极显著负相关(P<0.01)。(2)经1994-2011年的回检拟合和2012-2014年试报检验,卡尔曼模型的发生量预测综合平均误差为-88.63,平均绝对误差为217.72,均方根误差为605.04。发生量预测综合准确率为84.33%,平均历史拟合率为83.33%,各站卡尔曼模型的预报结果与实测值基本吻合,表明模型可以应用于稻纵卷叶螟候发生量的预测。%In this paper, the pentad systematic investigation data ofC. Medinalis at the four representative plant protection stations of four main rice-growing regions (including the rice-growing region of the south China, the rice-growing region of the southwestern China, the rice-growing region between the Nanling mountains and the Yantze River valley and the rice-growing region between the Yantze River valley and the Huaihe River valley) in China was collected from 1994 to 2014, the key meteorological factors influencing onC. Medinalis’ occurrence amount were screened out and Kalman filter algorithm was used to establish the short-term forecasting models ofC. Medinalis’ pentad occurrence amount at the four plant protection stations, including Quanzhou in the Guangxi Zhuang Autonomous Region, Xiushan in Chongqing city, Xiangyin in Hunan province and Zhangjiagang in Jiangsu province in the immigration and

  8. Instantaneous spectrum estimation of earthquake ground motions based on unscented Kalman filter method

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Representing earthquake ground motion as time varying ARMA model, the instantaneous spectrum can only be determined by the time varying coefficients of the corresponding ARMA model. In this paper, unscented Kalman filter is applied to estimate the time varying coefficients. The comparison between the estimation results of unscented Kalman filter and Kalman filter methods shows that unscented Kalman filter can more precisely represent the distribution of the spectral peaks in time-frequency plane than Kalman filter, and its time and frequency resolution is finer which ensures its better ability to track the local properties of earthquake ground motions and to identify the systems with nonlinearity or abruptness. Moreover, the estimation results of ARMA models with different orders indicate that the theoretical frequency resolving power ofARMA model which was usually ignored in former studies has great effect on the estimation precision of instantaneous spectrum and it should be taken as one of the key factors in order selection of ARMA model.

  9. Developing a Fundamental Model for an Integrated GPS/INS State Estimation System with Kalman Filtering

    Science.gov (United States)

    Canfield, Stephen

    1999-01-01

    This work will demonstrate the integration of sensor and system dynamic data and their appropriate models using an optimal filter to create a robust, adaptable, easily reconfigurable state (motion) estimation system. This state estimation system will clearly show the application of fundamental modeling and filtering techniques. These techniques are presented at a general, first principles level, that can easily be adapted to specific applications. An example of such an application is demonstrated through the development of an integrated GPS/INS navigation system. This system acquires both global position data and inertial body data, to provide optimal estimates of current position and attitude states. The optimal states are estimated using a Kalman filter. The state estimation system will include appropriate error models for the measurement hardware. The results of this work will lead to the development of a "black-box" state estimation system that supplies current motion information (position and attitude states) that can be used to carry out guidance and control strategies. This black-box state estimation system is developed independent of the vehicle dynamics and therefore is directly applicable to a variety of vehicles. Issues in system modeling and application of Kalman filtering techniques are investigated and presented. These issues include linearized models of equations of state, models of the measurement sensors, and appropriate application and parameter setting (tuning) of the Kalman filter. The general model and subsequent algorithm is developed in Matlab for numerical testing. The results of this system are demonstrated through application to data from the X-33 Michael's 9A8 mission and are presented in plots and simple animations.

  10. Experimental Evaluation of Torque Performance of Low Pass Filter and Extended Kalman Filter with Measured Torque for Induction Motor Drives

    Directory of Open Access Journals (Sweden)

    Ibrahim Mohd Alsofyani

    2015-02-01

    Full Text Available In this paper, two kinds of observers are proposed to investigate torque estimation. The first one is based on a voltage model represented with a low-pass filter (LPF; which is normally used as a replacement for a pure integrator to avoid integration drift problem due to dc offset or measurement error. The second estimator used is an extended Kalman filter (EKF as a current model, which puts into account all noise problems. Both estimation algorithms are investigated during the steady and transient states, tested under light load, and then compared with the measured mechanical torque. In all conditions, the torque estimation error for EKF has remained within a narrow error band and yielded minimum torque ripples, which motivate the use of the EKF estimation algorithm in high performance control drives of IMs for achieving high dynamic performance.

  11. A SAS/IML program using the Kalman filter for estimating state space models.

    Science.gov (United States)

    Gu, Fei; Yung, Yiu-Fai

    2013-03-01

    To help disseminate the knowledge and software implementation of a state space model (SSM), this article provides a SAS/IML (SAS Institute, 2010) program for estimating the parameters of general linear Gaussian SSMs using the Kalman filter algorithm. In order to use this program, the user should have SAS installed on a computer and have a valid license for SAS/IML. Since the code is completely open, it is expected that this program can be used not only by applied researchers, but also by quantitative methodologists who are interested in improving their methods and promoting SSM as a research instrument.

  12. A Quantised State Systems Approach for Jacobian Free Extended Kalman Filtering

    DEFF Research Database (Denmark)

    Alminde, Lars; Bendtsen, Jan Dimon; Stoustrup, Jakob

    2007-01-01

    Model based methods for control of intelligent autonomous systems rely on a state estimate being available. One of the most common methods to obtain a state estimate for non-linear systems is the Extended Kalman Filter (EKF) algorithm. In order to apply the EKF an expression must be available...... for the Jacobian of the driving function; for complex systems this can be difficult to obtain. This paper presents an EKF variation that makes use of integrated quantised state simulation to propagate the state and obtain a backward difference estimate of the Jacobian at a small computational cost. A simulation...

  13. Derivative free Kalman filtering used for orchard navigation

    DEFF Research Database (Denmark)

    Hansen, Søren; Bayramoglu, Enis; Andersen, Jens Christian

    2010-01-01

    In this paper the use of derivative free filters for mobile robot localisation is investigated. Three different filters are tested on real life data from an autonomous tractor running in an orchard environment. The localisation algorithm fuses odometry and gyro measurements with line features...

  14. 基于平方根UKF的SLAM算法%Square root Unscented Kalman Filter based SLAM

    Institute of Scientific and Technical Information of China (English)

    李树荣; 倪朋飞

    2011-01-01

    Simultaneous Localization and Mapping (SLAM) is concerned to be the key point to realize the real autonomy of mobile robot.Unscented Kalman Filter(UKP) is widely applied in SLAM problem because of its directly using of nonlinear modcl.Conceming that square root filter can ensure non-negative definite of the covariance matrix.This article introduces square root unscented Kalman filter into SLAM problem and ensures its stability.This algorithm also gains a more accurate estimation compared to UICF based SLAM.Simulation results show that this algorithm is effective.%同步定位与地图构建( SLAM)是移动机器人实现真正自主的关键,无迹卡尔曼滤波(UKF)由于直接利用系统非线性模型而在SLAM问题中得到广泛的应用.基于平方根滤波可以确保协方差矩阵的非负定的思想,将平方根UKF应用到SLAM问题中,确保了SLAM算法的稳定性,并得到了较高的估计精度.仿真结果表明,该算法是有效的.

  15. GPS Interference Mitigation Using Derivative-free Kalman Filter-based RNN

    Directory of Open Access Journals (Sweden)

    W. L. Mao

    2016-09-01

    Full Text Available The global positioning system (GPS with accurate positioning and timing properties has become integral part of all applications around the world. Radio frequency interference can significantly decrease the performance of GPS receivers or even completely prohibit the acquisition or tracking of satellites. The approaches of system performances that can be further enhanced by preprocessing to reject the jamming signal will be investigated. A recurrent neural network (RNN predictor for the GPS anti-jamming applications will be proposed. The adaptive RNN predictor is utilized to accurately predict the narrowband waveform based on an unscented Kalman filter (UKF-based algorithm. The UKF algorithm as a derivative-free alternative to the extended Kalman filter (EKF in the framework of state-estimation is adopted to achieve better performance in terms of convergence rate and quality of solution. The adaptive RNN filter can be successfully applied for the suppression of interference with a number of different narrowband formats, i.e. continuous wave interference (CWI, multi-tone CWI, swept CWI and pulsed CWI, to emulate realistic circumstances. Simulation results show that the proposed UKF-based scheme can offer the superior performances to suppress the interference over the conventional methods by computing mean squared prediction error (MSPE and signal-to-noise ratio (SNR improvements.

  16. A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems with Additive Noise

    Directory of Open Access Journals (Sweden)

    Yong Zhou

    2015-01-01

    Full Text Available The Kalman filter (KF, extended KF, and unscented KF all lack a self-adaptive capacity to deal with system noise. This paper describes a new adaptive filtering approach for nonlinear systems with additive noise. Based on the square-root unscented KF (SRUKF, traditional Maybeck’s estimator is modified and extended to nonlinear systems. The square root of the process noise covariance matrix Q or that of the measurement noise covariance matrix R is estimated straightforwardly. Because positive semidefiniteness of Q or R is guaranteed, several shortcomings of traditional Maybeck’s algorithm are overcome. Thus, the stability and accuracy of the filter are greatly improved. In addition, based on three different nonlinear systems, a new adaptive filtering technique is described in detail. Specifically, simulation results are presented, where the new filter was applied to a highly nonlinear model (i.e., the univariate nonstationary growth model (UNGM. The UNGM is compared with the standard SRUKF to demonstrate its superior filtering performance. The adaptive SRUKF (ASRUKF algorithm can complete direct recursion and calculate the square roots of the variance matrixes of the system state and noise, which ensures the symmetry and nonnegative definiteness of the matrixes and greatly improves the accuracy, stability, and self-adaptability of the filter.

  17. LHCb: Alignment of the LHCb Detector with Kalman Filter Fitted Tracks

    CERN Multimedia

    Amoraal, J; Hulsbergen, W; Needham, M; Nicolas, L; Pozzi, S; Raven, G; Vecchi, S

    2009-01-01

    We report on an implementation of a global chisquare algorithm for the simultaneous alignment of all tracking systems in the LHCb detector. Our algorithm uses hit residuals from the standard LHCb track fit which is based on a Kalman filter. The algorithm is implemented in the LHCb reconstruction framework and exploits the fact that all sensitive detector elements have the same geometry interface. A vertex constraint is implemented by fitting tracks to a common point and propagating the change in track parameters to the hit residuals. To remove unconstrained or poorly constrained degrees of freedom (so-called weak modes) the average movements of (subsets of) alignable detector elements can be fixed with Lagrange constraints. Alternatively, weak modes can be removed with a cutoff in the eigenvalue spectrum of the second derivative of the chisquare. As for all LHCb reconstruction and analysis software the configuration of the algorithm is done in python and gives detailed control over the selection of alignable ...

  18. Extended Kalman filtering for joint mitigation of phase and amplitude noise in coherent QAM systems.

    Science.gov (United States)

    Pakala, Lalitha; Schmauss, Bernhard

    2016-03-21

    We numerically investigate our proposed carrier phase and amplitude noise estimation (CPANE) algorithm using extend Kalman filter (EKF) for joint mitigation of linear and non-linear phase noise as well as amplitude noise on 4, 16 and 64 polarization multiplexed (PM) quadrature amplitude modulation (QAM) 224 Gb/s systems. The results are compared to decision directed (DD) carrier phase estimation (CPE), DD phase locked loop (PLL) and universal CPE (U-CPE) algorithms. Besides eliminating the necessity of phase unwrapping function, EKF-CPANE shows improved performance for both back-to-back (BTB) and transmission scenarios compared to the aforementioned algorithms. We further propose a weighted innovation approach (WIA) of the EKF-CPANE which gives an improvement of 0.3 dB in the Q-factor, compared to the original algorithm.

  19. Fast Kalman-like filtering for large-dimensional linear and Gaussian state-space models

    KAUST Repository

    Ait-El-Fquih, Boujemaa

    2015-08-13

    This paper considers the filtering problem for linear and Gaussian state-space models with large dimensions, a setup in which the optimal Kalman Filter (KF) might not be applicable owing to the excessive cost of manipulating huge covariance matrices. Among the most popular alternatives that enable cheaper and reasonable computation is the Ensemble KF (EnKF), a Monte Carlo-based approximation. In this paper, we consider a class of a posteriori distributions with diagonal covariance matrices and propose fast approximate deterministic-based algorithms based on the Variational Bayesian (VB) approach. More specifically, we derive two iterative KF-like algorithms that differ in the way they operate between two successive filtering estimates; one involves a smoothing estimate and the other involves a prediction estimate. Despite its iterative nature, the prediction-based algorithm provides a computational cost that is, on the one hand, independent of the number of iterations in the limit of very large state dimensions, and on the other hand, always much smaller than the cost of the EnKF. The cost of the smoothing-based algorithm depends on the number of iterations that may, in some situations, make this algorithm slower than the EnKF. The performances of the proposed filters are studied and compared to those of the KF and EnKF through a numerical example.

  20. The Kalman filter approach to inductively coupled plasma atomic emission spectrometry

    Science.gov (United States)

    Van Veen, E. H.; Bosch, S.; De Loos-Vollebregt, M. T. C.

    1994-07-01

    This article is an electronic publication in Spectrochimica Acta Electronica (SAE), the electronic section of Spectrochimica Acta Part B (SAB). The hardcopy text, comprising the main article and two appendices, is accompanied by a disk containing the compiled program, a reference manual and data files. The work deals with data handling in inductively coupled plasma atomic emission spectrometry (ICP-AES). With this technique, the analyte signal is superimposed on a background signal. When separating the signals by manual or automated three-point background correction, there are many instances in which the data reduction fails. Based on scans recorded in a fast-scanning mode and on a library of pure-component scans, the Kaiman filter approach models the emission in the spectral window (about 100 pm) of the analyte and mathematically solves the problem of background correction. By using a criterion-based algorithm to correct for optical instability, the uncertainty in the determination of the interferent line signal is eliminated. Therefore, the present filter implementation yields more accurate and precise results, especially in the case of line overlap. The Kalman filter Approach to Atomic Spectrometry (KAAS) software automatically processes Perkin-Elmer Plasma 1000/2000 text files, but can also handle ASCII data files. Practical and comprehensive examples are given to evoke the "Kalman filter feeling" in the crucial step of creating the emission model.

  1. Robust Distributed Kalman Filter for Wireless Sensor Networks with Uncertain Communication Channels

    Directory of Open Access Journals (Sweden)

    Du Yong Kim

    2012-01-01

    Full Text Available We address a state estimation problem over a large-scale sensor network with uncertain communication channel. Consensus protocol is usually used to adapt a large-scale sensor network. However, when certain parts of communication channels are broken down, the accuracy performance is seriously degraded. Specifically, outliers in the channel or temporal disconnection are avoided via proposed method for the practical implementation of the distributed estimation over large-scale sensor networks. We handle this practical challenge by using adaptive channel status estimator and robust L1-norm Kalman filter in design of the processor of the individual sensor node. Then, they are incorporated into the consensus algorithm in order to achieve the robust distributed state estimation. The robust property of the proposed algorithm enables the sensor network to selectively weight sensors of normal conditions so that the filter can be practically useful.

  2. Online damage detection in structural systems applications of proper orthogonal decomposition, and Kalman and particle filters

    CERN Document Server

    Eftekhar Azam, Saeed

    2014-01-01

    This monograph assesses in depth the application of recursive Bayesian filters in structural health monitoring. Although the methods and algorithms used here are well established in the field of automatic control, their application in the realm of civil engineering has to date been limited. The monograph is therefore intended as a reference for structural and civil engineers who wish to conduct research in this field. To this end, the main notions underlying the families of Kalman and particle filters are scrutinized through explanations within the text and numerous numerical examples. The main limitations to their application in monitoring of high-rise buildings are discussed, and a remedy based on a synergy of reduced order modeling (based on proper orthogonal decomposition) and Bayesian estimation is proposed. The performance and effectiveness of the proposed algorithm is demonstrated via pseudo-experimental evaluations.

  3. Parameter sensitivity of three Kalman Filter Schemes for the assimilation of tide guage data in coastal and self sea models

    DEFF Research Database (Denmark)

    Sørensen, Jacob Viborg Tornfeldt; Madsen, Henrik; Madsen, H.

    2006-01-01

    sensitivity study of three well known Kalman filter approaches for the assimilation of water levels in a three dimensional hydrodynamic modelling system. The filters considered are the ensemble Kalman filter (EnKF), the reduced rank square root Kalman filter (RRSQRT) and the steady Kalman filter...... is to be encouraged in this perspective. However, the predicted uncertainty of the assimilation results are sensitive to the parameters and hence must be applied with care. The sensitivity study further demonstrates the effectiveness of the steady Kalman filter in the given system as well as the great impact...

  4. Estimating ice-affected streamflow by extended Kalman filtering

    Science.gov (United States)

    Holtschlag, D.J.; Grewal, M.S.

    1998-01-01

    An extended Kalman filter was developed to automate the real-time estimation of ice-affected streamflow on the basis of routine measurements of stream stage and air temperature and on the relation between stage and streamflow during open-water (ice-free) conditions. The filter accommodates three dynamic modes of ice effects: sudden formation/ablation, stable ice conditions, and eventual elimination. The utility of the filter was evaluated by applying it to historical data from two long-term streamflow-gauging stations, St. John River at Dickey, Maine and Platte River at North Bend, Nebr. Results indicate that the filter was stable and that parameters converged for both stations, producing streamflow estimates that are highly correlated with published values. For the Maine station, logarithms of estimated streamflows are within 8% of the logarithms of published values 87.2% of the time during periods of ice effects and within 15% 96.6% of the time. Similarly, for the Nebraska station, logarithms of estimated streamflows are within 8% of the logarithms of published values 90.7% of the time and within 15% 97.7% of the time. In addition, the correlation between temporal updates and published streamflows on days of direct measurements at the Maine station was 0.777 and 0.998 for ice-affected and open-water periods, respectively; for the Nebraska station, corresponding correlations were 0.864 and 0.997.

  5. Using Kalman filters to reduce noise from RFID location system.

    Science.gov (United States)

    Abreu, Pedro Henriques; Xavier, José; Silva, Daniel Castro; Reis, Luís Paulo; Petry, Marcelo

    2014-01-01

    Nowadays, there are many technologies that support location systems involving intrusive and nonintrusive equipment and also varying in terms of precision, range, and cost. However, the developers some time neglect the noise introduced by these systems, which prevents these systems from reaching their full potential. Focused on this problem, in this research work a comparison study between three different filters was performed in order to reduce the noise introduced by a location system based on RFID UWB technology with an associated error of approximately 18 cm. To achieve this goal, a set of experiments was devised and executed using a miniature train moving at constant velocity in a scenario with two distinct shapes-linear and oval. Also, this train was equipped with a varying number of active tags. The obtained results proved that the Kalman Filter achieved better results when compared to the other two filters. Also, this filter increases the performance of the location system by 15% and 12% for the linear and oval paths respectively, when using one tag. For a multiple tags and oval shape similar results were obtained (11-13% of improvement).

  6. On a nonlinear Kalman filter with simplified divided difference approximation

    KAUST Repository

    Luo, Xiaodong

    2012-03-01

    We present a new ensemble-based approach that handles nonlinearity based on a simplified divided difference approximation through Stirling\\'s interpolation formula, which is hence called the simplified divided difference filter (sDDF). The sDDF uses Stirling\\'s interpolation formula to evaluate the statistics of the background ensemble during the prediction step, while at the filtering step the sDDF employs the formulae in an ensemble square root filter (EnSRF) to update the background to the analysis. In this sense, the sDDF is a hybrid of Stirling\\'s interpolation formula and the EnSRF method, while the computational cost of the sDDF is less than that of the EnSRF. Numerical comparison between the sDDF and the EnSRF, with the ensemble transform Kalman filter (ETKF) as the representative, is conducted. The experiment results suggest that the sDDF outperforms the ETKF with a relatively large ensemble size, and thus is a good candidate for data assimilation in systems with moderate dimensions. © 2011 Elsevier B.V. All rights reserved.

  7. A joint recovery scheme for carrier frequency offset and carrier phase noise using extended Kalman filter

    Science.gov (United States)

    Li, Linqian; Feng, Yiqiao; Zhang, Wenbo; Cui, Nan; Xu, Hengying; Tang, Xianfeng; Xi, Lixia; Zhang, Xiaoguang

    2017-07-01

    A joint carrier recovery scheme for polarization division multiplexing (PDM) coherent optical transmission system is proposed and demonstrated, in which the extended Kalman filter (EKF) is exploited to estimate and equalize the carrier frequency offset (CFO) and carrier phase noise (CPN) simultaneously. The proposed method is implemented and verified in the PDM-QPSK system and the PDM-16QAM system with the comparisons to conventional improved Mth-power (IMP) algorithm for CFO estimation, blind phase search (BPS) algorithm or Viterbi-Viterbi (V-V) algorithm for CPN recovery. It is demonstrated that the proposed scheme shows high CFO estimation accuracy, with absolute mean estimation error below 1.5 MHz. Meanwhile, the proposed method has the CFO tolerance of [±3 GHz] for PDM-QPSK system and [±0.9 GHz] for PDM-16QAM system. Compare with IMP/BPS and IMP/V-V, the proposed scheme can enhance the linewidth symbol duration product from 3 × 10-4 (IMP/BPS) and 2 × 10-4 (IMP/V-V) to 1 × 10-3 for PDM-QPSK, and from 1 × 10-4 (IMP/BPS) to 3 × 10-4 for PDM-16QAM, respectively, at the 1 dB optical signal-to-noise ratio (OSNR) penalty. The proposed Kalman filter also shows a fast convergence with only 100 symbols and much lower computational complexity.

  8. Constrained Kalman Filtering Via Density Function Truncation for Turbofan Engine Health Estimation

    Science.gov (United States)

    Simon, Dan; Simon, Donald L.

    2006-01-01

    Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops an analytic method of incorporating state variable inequality constraints in the Kalman filter. The resultant filter truncates the PDF (probability density function) of the Kalman filter estimate at the known constraints and then computes the constrained filter estimate as the mean of the truncated PDF. The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is demonstrated via simulation results obtained from a turbofan engine model. The turbofan engine model contains 3 state variables, 11 measurements, and 10 component health parameters. It is also shown that the truncated Kalman filter may be a more accurate way of incorporating inequality constraints than other constrained filters (e.g., the projection approach to constrained filtering).

  9. Constraining the Ensemble Kalman Filter for improved streamflow forecasting

    Science.gov (United States)

    Maxwell, Deborah; Jackson, Bethanna; McGregor, James

    2016-04-01

    Data assimilation techniques such as the Kalman Filter and its variants are often applied to hydrological models with minimal state volume/capacity constraints. Flux constraints are rarely, if ever, applied. Consequently, model states can be adjusted beyond physically reasonable limits, compromising the integrity of model output. In this presentation, we investigate the effect of constraining the Ensemble Kalman Filter (EnKF) on forecast performance. An EnKF implementation with no constraints is compared to model output with no assimilation, followed by a 'typical' hydrological implementation (in which mass constraints are enforced to ensure non-negativity and capacity thresholds of model states are not exceeded), and then a more tightly constrained implementation where flux as well as mass constraints are imposed to limit the rate of water movement within a state. A three year period (2008-2010) with no significant data gaps and representative of the range of flows observed over the fuller 1976-2010 record was selected for analysis. Over this period, the standard implementation of the EnKF (no constraints) contained eight hydrological events where (multiple) physically inconsistent state adjustments were made. All were selected for analysis. Overall, neither the unconstrained nor the "typically" mass-constrained forecasts were significantly better than the non-filtered forecasts; in fact several were significantly degraded. Flux constraints (in conjunction with mass constraints) significantly improved the forecast performance of six events relative to all other implementations, while the remaining two events showed no significant difference in performance. We conclude that placing flux as well as mass constraints on the data assimilation framework encourages physically consistent state updating and results in more accurate and reliable forward predictions of streamflow for robust decision-making. We also experiment with the observation error, and find that this

  10. Comparison of robust H∞ filter and Kalman filter for initial alignment of inertial navigation system

    Institute of Scientific and Technical Information of China (English)

    HAO Yan-ling; CHEN Ming-hui; LI Liang-jun; XU Bo

    2008-01-01

    There are many filtering methods that can be used for the initial alignment of an integrated inertial navigation system.This paper discussed the use of GPS,but focused on two kinds of filters for the initial alignment of an integrated strapdown inertial navigation system (SINS).One method is based on the Kalman filter (KF),and the other is based on the robust filter.Simulation results showed that the filter provides a quick transient response and a little more accurate estimate than KF,given substantial process noise or unknown noise statistics.So the robust filter is an effective and useful method for initial alignment of SINS.This research should make the use of SINS more popular,and is also a step for further research.

  11. Kalman Filtering with Intermittent Observations: Weak Convergence and Moderate Deviations

    CERN Document Server

    Kar, Soummya

    2009-01-01

    The paper considers the problem of Kalman filtering with intermittent observations, where the observation packet arrival process is modeled as a Bernoulli process. We start by extending the results of \\cite{Riccati-weakconv} to show that the sequence of random conditional error covariance matrices converges in distribution to a unique invariant distribution $\\mathbb{\\mu}^{\\bar{\\gamma}}$, as long as the packet arrival probability $\\bar{\\gamma}>0$. We completely characterize the sequence ${\\mathbb{\\mu}^{\\bar{\\gamma}}}$ of invariant distributions as $\\bar{\\gamma}\\uparrow 1$, by showing that the sequence ${\\mathbb{\\mu}^{\\bar{\\gamma}}}$ satisfies a moderate deviations principle (MDP) with a good rate function $I$, which is explicitly characterized. We then study the sequence of invariant distributions ${\\mathbb{\\mu}^{\\bar{\\gamma}}}$ as $\\bar{\\gamma}\\uparrow 1$. We show that, as $\\bar{\\gamma}\\uparrow 1$, ...

  12. Wet Refractivity Tomography with an hnproved Kalman-Filter Method

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    An improved retrieval method, which uses the solution with a Gaussian constraint as the initial state variables for the Kalman Filtering (KF) method, was developed to retrieve the wet refractivity profiles from slant wet delays (SWD) extracted by the double-differenced (DD) GPS method. The accuracy of the GPS-derived SWDs is also tested in this study against the measurements of a water vapor radiometer (WVR) and a weather model. It is concluded that the GPS-derived SWDs have similar accuracy to those measured with WVR and are much higher in quality than those derived from the weather model used. The developed method is used to retrieve the 3D wet refractivity distribution in the Hong Kong region. The retrieved profiles agree well with the radiosonde observations, with a difference of about 4 mm km-1 in the low levels. The accurate profiles obtained with this method are applicable in a number of meteorological applications.

  13. Tracking whole hand kinematics using extended Kalman filter.

    Science.gov (United States)

    Fu, Qiushi; Santello, Marco

    2010-01-01

    This paper describes the general procedure, model construction, and experimental results of tracking whole hand kinematics using extended Kalman filter (EKF) based on data recorded from active surface markers. We used a hand model with 29 degrees of freedom that consists of hand global posture, wrist, and digits. The marker protocol had 4 markers on the distal forearm and 20 markers on the dorsal surface of the joints of the digits. To reduce computational load, we divided the state space into four sub-spaces, each of which were estimated with an EKF in a specific order. We tested our framework and found reasonably accurate results (2-4 mm tip position error) when sampling tip to tip pinch at 120 Hz.

  14. Model Calibration of Exciter and PSS Using Extended Kalman Filter

    Energy Technology Data Exchange (ETDEWEB)

    Kalsi, Karanjit; Du, Pengwei; Huang, Zhenyu

    2012-07-26

    Power system modeling and controls continue to become more complex with the advent of smart grid technologies and large-scale deployment of renewable energy resources. As demonstrated in recent studies, inaccurate system models could lead to large-scale blackouts, thereby motivating the need for model calibration. Current methods of model calibration rely on manual tuning based on engineering experience, are time consuming and could yield inaccurate parameter estimates. In this paper, the Extended Kalman Filter (EKF) is used as a tool to calibrate exciter and Power System Stabilizer (PSS) models of a particular type of machine in the Western Electricity Coordinating Council (WECC). The EKF-based parameter estimation is a recursive prediction-correction process which uses the mismatch between simulation and measurement to adjust the model parameters at every time step. Numerical simulations using actual field test data demonstrate the effectiveness of the proposed approach in calibrating the parameters.

  15. Predicting breeding values in animals by kalman filter

    DEFF Research Database (Denmark)

    Karacaören, Burak; Janss, Luc; Kadarmideen, Haja

    2012-01-01

    The aim of this study was to investigate usefulness of Kalman Filter (KF) Random Walk methodology (KF-RW) for prediction of breeding values in animals. We used body condition score (BCS) from dairy cattle for illustrating use of KF-RW. BCS was measured by Swiss Holstein Breeding Association during...... May 2004-March 2005 for 7 times approximately at monthly intervals from dairy cows (n=80) stationed at the Chamau research farm of Eidgenössische Technische Hochschule (ETH), Switzerland. Benefits of KF were demonstrated using random walk models via simulations. Breeding values were predicted over...... for variance components were found (with standard errors) 0.03 (0.006) for animal genetic variance 0.04 (0.007) for permanent environmental variance and 0.21 (0.02) for error variance. Since KF gives online estimation of breeding values and does not need to store or invert matrices, this methodology could...

  16. Alignment of the LHCb detector with Kalman filter fitted tracks

    CERN Document Server

    Amoraal, J M

    2009-01-01

    The LHCb detector, operating at the Large Hadron Collider at CERN, is a single arm spectrometer optimised for the detection of forward b and anti-b production for b physics studies. The reconstruction of vertices and tracks is done by silicon micro-strip and gaseous straw-tube based detectors. To obtain excellent momentum, mass and vertex resolutions, the detectors need to be aligned well within the hit resolution for a given detector. We present a general and easy to configure alignment framework which uses the closed from method of alignment with Kalman filter fitted tracks to determine the alignment parameters. This allows us to use the standard LHCb track model and fit, and correctly take complexities such as multiple scattering and energy loss corrections into account. With this framework it is possible to align any detector for any degree of freedom.

  17. Kalman Filter for Mass Property and Thrust Identification (MMS)

    Science.gov (United States)

    Queen, Steven

    2015-01-01

    The Magnetospheric Multiscale (MMS) mission consists of four identically instrumented, spin-stabilized observatories, elliptically orbiting the Earth in a tetrahedron formation. For the operational success of the mission, on-board systems must be able to deliver high-precision orbital adjustment maneuvers. On MMS, this is accomplished using feedback from on-board star sensors in tandem with accelerometers whose measurements are dynamically corrected for errors associated with a spinning platform. In order to determine the required corrections to the measured acceleration, precise estimates of attitude, rate, and mass-properties is necessary. To this end, both an on-board and ground-based Multiplicative Extended Kalman Filter (MEKF) were formulated and implemented in order to estimate the dynamic and quasi-static properties of the spacecraft.

  18. Damping strapdown inertial navigation system based on a Kalman filter

    Science.gov (United States)

    Zhao, Lin; Li, Jiushun; Cheng, Jianhua; Hao, Yong

    2016-11-01

    A damping strapdown inertial navigation system (DSINS) can effectively suppress oscillation errors of strapdown inertial navigation systems (SINSs) and improve the navigation accuracy of SINSs. Aiming at overcoming the disadvantages of traditional damping methods, a DSINS, based on a Kalman filter (KF), is proposed in this paper. Using the measurement data of accelerometers and calculated navigation parameters during the navigation process, the expression of the observation equation is derived. The calculation process of the observation in both the internal damping state and the external damping state is presented. Finally, system oscillation errors are compensated by a KF. Simulation and test results show that, compared with traditional damping methods, the proposed method can reduce system overshoot errors and shorten the convergence time of oscillation errors effectively.

  19. Kalman Filter Track Fits and Track Breakpoint Analysis

    CERN Document Server

    Astier, Pierre; Cousins, R D; Letessier-Selvon, A A; Popov, B A; Vinogradova, T G; Astier, Pierre; Cardini, Alessandro; Cousins, Robert D.; Letessier-Selvon, Antoine; Popov, Boris A.; Vinogradova, Tatiana

    2000-01-01

    We give an overview of track fitting using the Kalman filter method in the NOMAD detector at CERN, and emphasize how the wealth of by-product information can be used to analyze track breakpoints (discontinuities in track parameters caused by scattering, decay, etc.). After reviewing how this information has been previously exploited by others, we describe extensions which add power to breakpoint detection and characterization. We show how complete fits to the entire track, with breakpoint parameters added, can be easily obtained from the information from unbroken fits. Tests inspired by the Fisher F-test can then be used to judge breakpoints. Signed quantities (such as change in momentum at the breakpoint) can supplement unsigned quantities such as the various chisquares. We illustrate the method with electrons from real data, and with Monte Carlo simulations of pion decays.

  20. Prediction of Lumen Output and Chromaticity Shift in LEDs Using Kalman Filter and Extended Kalman Filter Based Models

    Energy Technology Data Exchange (ETDEWEB)

    Lall, Pradeep; Wei, Junchao; Davis, J Lynn

    2014-06-24

    Abstract— Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. Life prediction of L70 life for the LEDs used in SSL luminaires from KF and EKF based models have

  1. Kalman-median Compound Filter for Gaussian and Impulse Noise Reduction on Digital Images

    OpenAIRE

    山森, 一人; 山田, 義治; 相川, 勝

    2013-01-01

    This paper proposes an image restoration method from degraded images which include additive gaussian noise and impulse noise. This method tries to achieve image restoration by using combination of canonical state space model kalman filter and median filter. Kalman filter estimates internal state of a dynamic system based on system model. The canonical state space models are described by two equations; state equation that expresses a transition process of the region including the focusing pixe...

  2. Robust SLAM using square-root cubature Kalman filter and Huber’ s GM-estimator

    Institute of Scientific and Technical Information of China (English)

    徐巍军

    2016-01-01

    Mobile robot systems performing simultaneous localization and mapping ( SLAM) are generally plagued by non-Gaussian noise.To improve both accuracy and robustness under non-Gaussian meas-urement noise, a robust SLAM algorithm is proposed.It is based on the square-root cubature Kal-man filter equipped with a Huber’ s generalized maximum likelihood estimator ( GM-estimator) .In particular, the square-root cubature rule is applied to propagate the robot state vector and covariance matrix in the time update, the measurement update and the new landmark initialization stages of the SLAM.Moreover, gain weight matrices with respect to the measurement residuals are calculated by utilizing Huber’ s technique in the measurement update step.The measurement outliers are sup-pressed by lower Kalman gains as merging into the system.The proposed algorithm can achieve bet-ter performance under the condition of non-Gaussian measurement noise in comparison with benchmark algorithms.The simulation results demonstrate the advantages of the proposed SLAM algorithm.

  3. Interlaced optimal-REQUEST and unscented Kalman filtering for attitude determination

    Institute of Scientific and Technical Information of China (English)

    Quan Wei; Xu Liang; Zhang Huijuan; Fang Jiancheng

    2013-01-01

    Aimed at low accuracy of attitude determination because of using low-cost components which may result in non-linearity in integrated attitude determination systems,a novel attitude determination algorithm using vector observations and gyro measurements is presented.The various features of the unscented Kalman filter (UKF) and optimal-REQUEST (quaternion estimator) algorithms are introduced for attitude determination.An interlaced filtering method is presented for the attitude determination of nano-spacecraft by setting the quaternion as the attitude representation,using the UKF and optimal-REQUEST to estimate the gyro drifts and the quaternion,respectively.The optimal-REQUEST and UKF are not isolated from each other.When the optimal-REQUEST algorithm estimates the attitude quaternion,the gyro drifts are estimated by the UKF algorithm synchronously by using the estimated attitude quaternion.Furthermore,the speed of attitude determination is improved by setting the state dimension to three.Experimental results show that the presented method has higher performance in attitude determination compared to the UKF algorithm and the traditional interlaced filtering method and can estimate the gyro drifts quickly.

  4. Delay Kalman Filter to Estimate the Attitude of a Mobile Object with Indoor Magnetic Field Gradients

    Directory of Open Access Journals (Sweden)

    Christophe Combettes

    2016-05-01

    Full Text Available More and more services are based on knowing the location of pedestrians equipped with connected objects (smartphones, smartwatches, etc.. One part of the location estimation process is attitude estimation. Many algorithms have been proposed but they principally target open space areas where the local magnetic field equals the Earth’s field. Unfortunately, this approach is impossible indoors, where the use of magnetometer arrays or magnetic field gradients has been proposed. However, current approaches omit the impact of past state estimates on the current orientation estimate, especially when a reference field is computed over a sliding window. A novel Delay Kalman filter is proposed in this paper to integrate this time correlation: the Delay MAGYQ. Experimental assessment, conducted in a motion lab with a handheld inertial and magnetic mobile unit, shows that the novel filter better estimates the Euler angles of the handheld device with an 11.7° mean error on the yaw angle as compared to 16.4° with a common Additive Extended Kalman filter.

  5. Rhythmic Extended Kalman Filter for Gait Rehabilitation Motion Estimation and Segmentation.

    Science.gov (United States)

    Joukov, Vladimir; Bonnet, Vincent; Karg, Michelle; Venture, Gentiane; Kulic, Dana

    2017-01-26

    This work proposes a method to enable the use of non-intrusive, small, wearable, wireless sensors to estimate the pose of the lower body during gait and other periodic motions and to extract objective performance measures useful for physiotherapy. The Rhythmic Extended Kalman Filter (Rhythmic- EKF) algorithm is developed to estimate the pose, learn an individualized model of periodic movement over time, and use the learned model to improve pose estimation. The proposed approach learns a canonical dynamical system model of the movement during online observation, which is used to accurately model the acceleration during pose estimation. The canonical dynamical system models the motion as a periodic signal. The estimated phase and frequency of the motion also allow the proposed approach to segment the motion into repetitions and extract useful features such as gait symmetry, step length, and mean joint movement and variance. The algorithm is shown to outperform the extended Kalman filter in simulation, on healthy participant data, and stroke patient data. For the healthy participant marching dataset, the Rhythmic-EKF improves joint acceleration and velocity estimates over regular EKF by 40% and 37% respectively, estimates joint angles with 2.4° RMSE, and segments the motion into repetitions with 96% accuracy.

  6. Filtering remotely sensed chlorophyll concentrations in the Red Sea using a space-time covariance model and a Kalman filter

    KAUST Repository

    Dreano, Denis

    2015-04-27

    A statistical model is proposed to filter satellite-derived chlorophyll concentration from the Red Sea, and to predict future chlorophyll concentrations. The seasonal trend is first estimated after filling missing chlorophyll data using an Empirical Orthogonal Function (EOF)-based algorithm (Data Interpolation EOF). The anomalies are then modeled as a stationary Gaussian process. A method proposed by Gneiting (2002) is used to construct positive-definite space-time covariance models for this process. After choosing an appropriate statistical model and identifying its parameters, Kriging is applied in the space-time domain to make a one step ahead prediction of the anomalies. The latter serves as the prediction model of a reduced-order Kalman filter, which is applied to assimilate and predict future chlorophyll concentrations. The proposed method decreases the root mean square (RMS) prediction error by about 11% compared with the seasonal average.

  7. Zero Gyro Kalman Filtering in the Presence of a Reaction Wheel Failure

    Science.gov (United States)

    Hur-Diaz, Sun; Wirzburger, John; Smith, Dan; Myslinski, Mike

    2007-01-01

    Typical implementation of Kalman filters for spacecraft attitude estimation involves the use of gyros for three-axis rate measurements. When there are less than three axes of information available, the accuracy of the Kalman filter depends highly on the accuracy of the dynamics model. This is particularly significant during the transient period when a reaction wheel with a high momentum fails, is taken off-line, and spins down. This paper looks at how a reaction wheel failure can affect the zero-gyro Kalman filter performance for the Hubble Space Telescope and what steps are taken to minimize its impact.

  8. A Computationally Efficient and Robust Implementation of the Continuous-Discrete Extended Kalman Filter

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Thomsen, Per Grove; Madsen, Henrik;

    2007-01-01

    We present a novel numerically robust and computationally efficient extended Kalman filter for state estimation in nonlinear continuous-discrete stochastic systems. The resulting differential equations for the mean-covariance evolution of the nonlinear stochastic continuous-discrete time systems...... are solved efficiently using an ESDIRK integrator with sensitivity analysis capabilities. This ESDIRK integrator for the mean- covariance evolution is implemented as part of an extended Kalman filter and tested on a PDE system. For moderate to large sized systems, the ESDIRK based extended Kalman filter...

  9. Hypersonic entry vehicle state estimation using nonlinearity-based adaptive cubature Kalman filters

    Science.gov (United States)

    Sun, Tao; Xin, Ming

    2017-05-01

    Guidance, navigation, and control of a hypersonic vehicle landing on the Mars rely on precise state feedback information, which is obtained from state estimation. The high uncertainty and nonlinearity of the entry dynamics make the estimation a very challenging problem. In this paper, a new adaptive cubature Kalman filter is proposed for state trajectory estimation of a hypersonic entry vehicle. This new adaptive estimation strategy is based on the measure of nonlinearity of the stochastic system. According to the severity of nonlinearity along the trajectory, the high degree cubature rule or the conventional third degree cubature rule is adaptively used in the cubature Kalman filter. This strategy has the benefit of attaining higher estimation accuracy only when necessary without causing excessive computation load. The simulation results demonstrate that the proposed adaptive filter exhibits better performance than the conventional third-degree cubature Kalman filter while maintaining the same performance as the uniform high degree cubature Kalman filter but with lower computation complexity.

  10. Global Systems for Mobile Position Tracking Using Kalman and Lainiotis Filters

    Directory of Open Access Journals (Sweden)

    Nicholas Assimakis

    2014-01-01

    Full Text Available We present two time invariant models for Global Systems for Mobile (GSM position tracking, which describe the movement in x-axis and y-axis simultaneously or separately. We present the time invariant filters as well as the steady state filters: the classical Kalman filter and Lainiotis Filter and the Join Kalman Lainiotis Filter, which consists of the parallel usage of the two classical filters. Various implementations are proposed and compared with respect to their behavior and to their computational burden: all time invariant and steady state filters have the same behavior using both proposed models but have different computational burden. Finally, we propose a Finite Impulse Response (FIR implementation of the Steady State Kalman, and Lainiotis filters, which does not require previous estimations but requires a well-defined set of previous measurements.

  11. Acoustic tomography of the atmosphere using iterated unscented Kalman filter

    Science.gov (United States)

    Kolouri, Soheil

    Tomography approaches are of great interests because of their non-intrusive nature and their ability to generate a significantly larger amount of data in comparison to the in-situ measurement method. Acoustic tomography is an approach which reconstructs the unknown parameters that affect the propagation of acoustic rays in a field of interest by studying the temporal characteristics of the propagation. Acoustic tomography has been used in several different disciplines such as biomedical imaging, oceanographic studies and atmospheric studies. The focus of this thesis is to study acoustic tomography of the atmosphere in order to reconstruct the temperature and wind velocity fields in the atmospheric surface layer using the travel-times collected from several pairs of transmitter and receiver sensors distributed in the field. Our work consists of three main parts. The first part of this thesis is dedicated to reviewing the existing methods for acoustic tomography of the atmosphere, namely statistical inversion (SI), time dependent statistical inversion (TDSI), simultaneous iterative reconstruction technique (SIRT), and sparse recovery framework. The properties of these methods are then explained extensively and their shortcomings are also mentioned. In the second part of this thesis, a new acoustic tomography method based on Unscented Kalman Filter (UKF) is introduced in order to address some of the shortcomings of the existing methods. Using the UKF, the problem is cast as a state estimation problem in which the temperature and wind velocity fields are the desired states to be reconstructed. The field is discretized into several grids in which the temperature and wind velocity fields are assumed to be constant. Different models, namely random walk, first order 3-D autoregressive (AR) model, and 1-D temporal AR model are used to capture the state evolution in time-space. Given the time of arrival (TOA) equation for acoustic propagation as the observation equation, the

  12. Accurate 3D maps from depth images and motion sensors via nonlinear Kalman filtering

    CERN Document Server

    Hervier, Thibault; Goulette, François

    2012-01-01

    This paper investigates the use of depth images as localisation sensors for 3D map building. The localisation information is derived from the 3D data thanks to the ICP (Iterative Closest Point) algorithm. The covariance of the ICP, and thus of the localization error, is analysed, and described by a Fisher Information Matrix. It is advocated this error can be much reduced if the data is fused with measurements from other motion sensors, or even with prior knowledge on the motion. The data fusion is performed by a recently introduced specific extended Kalman filter, the so-called Invariant EKF, and is directly based on the estimated covariance of the ICP. The resulting filter is very natural, and is proved to possess strong properties. Experiments with a Kinect sensor and a three-axis gyroscope prove clear improvement in the accuracy of the localization, and thus in the accuracy of the built 3D map.

  13. Research on Continuous Positioning with Insufficient Visible Satellites Based on Kalman Filter Algorithm%基于卡尔曼滤波算法卫星数不足情况下的连续定位

    Institute of Scientific and Technical Information of China (English)

    谢翔; 郭际明; 曹文涛; 周命端

    2013-01-01

    The Kalman filtering model was introduced in this paper. And it was used as a parameter estimation method to realize dynamic pseudo-range single positioning solutions. It also resolved partial observations with insufficient visible satellites. And the positioning results were compared. The test result indicates that Kalman filer model help to keep positioning continuously and guarantee a certain extent precision.%介绍了卡尔曼滤波模型,采用C#编程语言建立了程序模块,并将其作为参数估计方法对一组动态数据进行了伪距单点定位解算;将部分历元时刻卫星数设置为少于4颗进行解算,并比较了其定位结果与原始结果的差异.实验结果表明,采用卡尔曼滤波模型能够在卫星数不足的情况下在一定时间内保持定位的连续性,并能保证一定精度.

  14. Derivative free Kalman filtering used for orchard navigation

    DEFF Research Database (Denmark)

    Hansen, Søren; Bayramoglu, Enis; Andersen, Jens Christian

    2010-01-01

    representing the surrounding fruit trees. The line features are created on basis of 2D laser scanner data by a least square algorithm. The Matlab (R) toolbox Kalmtool is used for easy switching between different filter implementations without the need for changing the base structure of the system....

  15. Multi-channel Kalman filters for active noise control

    NARCIS (Netherlands)

    Ophem, S. van; Berkhoff, A.P.

    2013-01-01

    By formulating the feed-forward broadband active noise control problem as a state estimation problem it is possible to achieve a faster rate of convergence than the filtered reference least mean squares algorithm and possibly also a better tracking performance. A multiple input/multiple output

  16. Multi-channel Kalman filters for active noise control

    NARCIS (Netherlands)

    van Ophem, S.; Berkhoff, Arthur P.

    By formulating the feed-forward broadband active noise control problem as a state estimation problem it is possible to achieve a faster rate of convergence than the filtered reference least mean squares algorithm and possibly also a better tracking performance. A multiple input/multiple output

  17. A novel extended Kalman filter for a class of nonlinear systems

    Institute of Scientific and Technical Information of China (English)

    DONG Zhe; YOU Zheng

    2006-01-01

    Estimation of the state variables of nonlinear systems is one of the fundamental and significant problems in control and signal processing. A new extended Kalman filtering approach for a class of nonlinear discrete-time systems in engineering is presented in this paper. In contrast to the celebrated extended Kalman filter (EKF), there is no linearization operation in the design procedure of the filter, and the parameters of the filter are obtained through minimizing a proper upper bound of the mean-square estimation error. Simulation results show that this filter can provide higher estimation precision than that provided by the EKF.

  18. Adaptive UAV attitude estimation employing unscented Kalman Filter, FOAM and low-cost MEMS sensors.

    Science.gov (United States)

    de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos

    2012-01-01

    Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.

  19. Decentralized neural identifier and control for nonlinear systems based on extended Kalman filter.

    Science.gov (United States)

    Castañeda, Carlos E; Esquivel, P

    2012-07-01

    A time-varying learning algorithm for recurrent high order neural network in order to identify and control nonlinear systems which integrates the use of a statistical framework is proposed. The learning algorithm is based in the extended Kalman filter, where the associated state and measurement noises covariance matrices are composed by the coupled variance between the plant states. The formulation allows identification of interactions associate between plant state and the neural convergence. Furthermore, a sliding window-based method for dynamical modeling of nonstationary systems is presented to improve the neural identification in the proposed methodology. The efficiency and accuracy of the proposed method is assessed to a five degree of freedom (DOF) robot manipulator where based on the time-varying neural identifier model, the decentralized discrete-time block control and sliding mode techniques are used to design independent controllers and develop the trajectory tracking for each DOF.

  20. Tracking Road Centerlines from Remotely Sensed Imagery Using Mean Shift and Kalman Filtering

    Directory of Open Access Journals (Sweden)

    CAO Fanzhi

    2016-02-01

    Full Text Available Road tracking based on template matching is one class of practical methods of road extraction. However, the conventional methods of template matching mainly utilize correlation coefficient as the similarity measure. As a result, these algorithms are sensitive to occlusions caused by vehicles and trees and are unsuitable for road extraction from high-resolution remotely sensed imagery. To address this problem, this paper designs a road center matching algorithm based on mean shift utilizing a robust similarity measure, which overcomes the sensitivity of correlation coefficient matching to occlusions; then Kalman filter is utilized to track road centerlines from high-resolution remotely sensed imagery. Experimental results demonstrate that the proposed method can extract road centerlines from high-resolution remotely sensed imagery accurately and is robust to occlusions caused by vehicles and trees.

  1. Adaptive Kalman filter for indoor localization using Bluetooth Low Energy and inertial measurement unit.

    Science.gov (United States)

    Yoon, Paul K; Zihajehzadeh, Shaghayegh; Bong-Soo Kang; Park, Edward J

    2015-08-01

    This paper proposes a novel indoor localization method using the Bluetooth Low Energy (BLE) and an inertial measurement unit (IMU). The multipath and non-line-of-sight errors from low-power wireless localization systems commonly result in outliers, affecting the positioning accuracy. We address this problem by adaptively weighting the estimates from the IMU and BLE in our proposed cascaded Kalman filter (KF). The positioning accuracy is further improved with the Rauch-Tung-Striebel smoother. The performance of the proposed algorithm is compared against that of the standard KF experimentally. The results show that the proposed algorithm can maintain high accuracy for position tracking the sensor in the presence of the outliers.

  2. Adaptive Kalman Filter of Transfer Alignment with Un-modeled Wing Flexure of Aircraft

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The alignment accuracy of the strap-down inertial navigation system (SINS) of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.

  3. A new approach to UTC calculation by means of the Kalman filter

    Science.gov (United States)

    Parisi, Federica; Panfilo, Gianna

    2016-10-01

    In this paper a new approach to Coordinated Universal Time (UTC) calculation is presented by means of the Kalman filter. An ensemble of atomic clocks participating in UTC is selected for analyzing and testing the potentiality of this new method.

  4. The use of the ensemble Kalman filter for production management in the presence of impermeable obstructions

    National Research Council Canada - National Science Library

    Saad, George; Azizi, Fouad

    ... the sweep efficiency inside the reservoir. Controlling the flood front dynamics is achieved by coupling an ensemble Kalman filter scheme with a two-phase immiscible flow reservoir simulator and thus relying on a set of observational data...

  5. Comparison of different Kalman filter approaches in deriving time varying connectivity from EEG data.

    Science.gov (United States)

    Ghumare, Eshwar; Schrooten, Maarten; Vandenberghe, Rik; Dupont, Patrick

    2015-08-01

    Kalman filter approaches are widely applied to derive time varying effective connectivity from electroencephalographic (EEG) data. For multi-trial data, a classical Kalman filter (CKF) designed for the estimation of single trial data, can be implemented by trial-averaging the data or by averaging single trial estimates. A general linear Kalman filter (GLKF) provides an extension for multi-trial data. In this work, we studied the performance of the different Kalman filtering approaches for different values of signal-to-noise ratio (SNR), number of trials and number of EEG channels. We used a simulated model from which we calculated scalp recordings. From these recordings, we estimated cortical sources. Multivariate autoregressive model parameters and partial directed coherence was calculated for these estimated sources and compared with the ground-truth. The results showed an overall superior performance of GLKF except for low levels of SNR and number of trials.

  6. INFLUENCE OF STOCHASTIC NOISE STATISTICS ON KALMAN FILTER PERFORMANCE BASED ON VIDEO TARGET TRACKING

    Institute of Scientific and Technical Information of China (English)

    Chen Ken; Napolitano; Zhang Yun; Li Dong

    2010-01-01

    The system stochastic noises involved in Kalman filtering are preconditioned on being ideally white and Gaussian distributed. In this research,efforts are exerted on exploring the influence of the noise statistics on Kalman filtering from the perspective of video target tracking quality. The correlation of tracking precision to both the process and measurement noise covariance is investigated; the signal-to-noise power density ratio is defined; the contribution of predicted states and measured outputs to Kalman filter behavior is discussed; the tracking precision relative sensitivity is derived and applied in this study case. The findings are expected to pave the way for future study on how the actual noise statistics deviating from the assumed ones impacts on the Kalman filter optimality and degradation in the application of video tracking.

  7. Recursive least squares method of regression coefficients estimation as a special case of Kalman filter

    Science.gov (United States)

    Borodachev, S. M.

    2016-06-01

    The simple derivation of recursive least squares (RLS) method equations is given as special case of Kalman filter estimation of a constant system state under changing observation conditions. A numerical example illustrates application of RLS to multicollinearity problem.

  8. 基于中心差分扩展卡尔曼滤波算法的三相感应电机转速和转矩的估计%Estimation of Speed and Torque of Three-phase Induction Motor Based on Central Difference Extended Kalman Filter Algorithm

    Institute of Scientific and Technical Information of China (English)

    常晓丽; 付巍; 梁慧; 李锦荣; 闫文博

    2015-01-01

    In the process of there-phase induction motor speed and torque estimation,the data collec-tion is vulnerable to the outside interference easily,which leads to large deviations.Speed and torque es-timation method of there-phase induction motor based on central difference extended Kalman filter was proposed.At first,deduced there-phase induction motor state equations and output equations in theα-βcoordinate system with three-phase induction motor stator and rotor currents as state variables.Then u-sing central difference extended kalman filter algorithm for three-phase induction motor speed and torque estimation.The simulation results show that,compares with extended kalman filter,central difference extended kalman filter algorithm has better follow-up performance when motor running state changes, and has faster rate of convergence.Thus speed and torque of there-phase induction motor can be esti-mated precisely.%针对三相感应电机在转速和转矩测试过程中,存在采集数据易受外界干扰而导致转速和转矩等估计值出现较大偏差的问题,提出采用中心差心扩展卡尔曼滤波算法实现对三相感应电机转速和转矩的估计。首先,以三相感应电机的定子和转子电流作为状态变量,推导出了三相感应电机在α-β坐标系下的状态方程和输出方程。然后,采用中心差分扩展卡尔曼滤波算法实现对三相感应电机转速和转矩的解算。仿真结果表明,与扩展卡尔曼滤波算法相比,中心差分扩展卡尔曼滤波算法对电机运行状态的变化具有更好的跟随性能及更快的收敛速度,能够准确的估计三相感应电机的转速和转矩。

  9. Autonomous robotic capture of non-cooperative target by adaptive extended Kalman filter based visual servo

    Science.gov (United States)

    Dong, Gangqi; Zhu, Zheng H.

    2016-05-01

    This paper presents a real-time, vision-based algorithm for the pose and motion estimation of non-cooperative targets and its application in visual servo robotic manipulator to perform autonomous capture. A hybrid approach of adaptive extended Kalman filter and photogrammetry is developed for the real-time pose and motion estimation of non-cooperative targets. Based on the pose and motion estimates, the desired pose and trajectory of end-effector is defined and the corresponding desired joint angles of the robotic manipulator are derived by inverse kinematics. A close-loop visual servo control scheme is then developed for the robotic manipulator to track, approach and capture the target. Validating experiments are designed and performed on a custom-built six degrees of freedom robotic manipulator with an eye-in-hand configuration. The experimental results demonstrate the feasibility, effectiveness and robustness of the proposed adaptive extended Kalman filter enabled pose and motion estimation and visual servo strategy.

  10. Data assimilation with the ensemble Kalman filter in a numerical model of the North Sea

    Science.gov (United States)

    Ponsar, Stéphanie; Luyten, Patrick; Dulière, Valérie

    2016-08-01

    Coastal management and maritime safety strongly rely on accurate representations of the sea state. Both dynamical models and observations provide abundant pieces of information. However, none of them provides the complete picture. The assimilation of observations into models is one way to improve our knowledge of the ocean state. Its application in coastal models remains challenging because of the wide range of temporal and spatial variabilities of the processes involved. This study investigates the assimilation of temperature profiles with the ensemble Kalman filter in 3-D North Sea simulations. The model error is represented by the standard deviation of an ensemble of model states. Parameters' values for the ensemble generation are first computed from the misfit between the data and the model results without assimilation. Then, two square root algorithms are applied to assimilate the data. The impact of data assimilation on the simulated temperature is assessed. Results show that the ensemble Kalman filter is adequate for improving temperature forecasts in coastal areas, under adequate model error specification.

  11. Switching Kalman filter based methods for apnea bradycardia detection from ECG signals.

    Science.gov (United States)

    Montazeri Ghahjaverestan, Nasim; Shamsollahi, Mohammad B; Ge, Di; Hernández, Alfredo I

    2015-09-01

    Apnea bradycardia (AB) is an outcome of apnea occurrence in preterm infants and is an observable phenomenon in cardiovascular signals. Early detection of apnea in infants under monitoring is a critical challenge for the early intervention of nurses. In this paper, we introduce two switching Kalman filter (SKF) based methods for AB detection using electrocardiogram (ECG) signal.The first SKF model uses McSharry's ECG dynamical model integrated in two Kalman filter (KF) models trained for normal and AB intervals. Whereas the second SKF model is established by using only the RR sequence extracted from ECG and two AR models to be fitted in normal and AB intervals. In both SKF approaches, a discrete state variable called a switch is considered that chooses one of the models (corresponding to normal and AB) during the inference phase. According to the probability of each model indicated by this switch, the model with larger probability determines the observation label at each time instant.It is shown that the method based on ECG dynamical model can be effectively used for AB detection. The detection performance is evaluated by comparing statistical metrics and the amount of time taken to detect AB compared with the annotated onset. The results demonstrate the superiority of this method, with sensitivity and specificity 94.74[Formula: see text] and 94.17[Formula: see text], respectively. The presented approaches may therefore serve as an effective algorithm for monitoring neonates suffering from AB.

  12. Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter

    Institute of Scientific and Technical Information of China (English)

    Sheng JIN; Dian-hai WANG; Cheng XU; Dong-fang MA

    2013-01-01

    In this paper; a prediction model is developed that combines a Gaussian mixture model (GMM) and a Kalman filter for online forecasting of traffic safety on expressways.Raw time-to-collision (TTC) samples are divided into two categories:those representing vehicles in risky situations and those in safe situations.Then,the GMM is used to model the bimodal distribution of the TTC samples,and the maximum likelihood (ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization (EM) algorithm.We propose a new traffic safety indicator,named the proportion of exposure to traffic conflicts (PETTC),for assessing the risk and predicting the safety of expressway traffic.A Kalman filter is applied to forecast the short-term safety indicator,PETTC,and solves the online safety prediction problem.A dataset collected from four different expressway locations is used for performance estimation.The test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different datasets.These results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems.

  13. Kalman filtering techniques for reducing variance of digital speckle displacement measurement noise

    Institute of Scientific and Technical Information of China (English)

    Donghui Li; Li Guo

    2006-01-01

    @@ Target dynamics are assumed to be known in measuring digital speckle displacement. Use is made of a simple measurement equation, where measurement noise represents the effect of disturbances introduced in measurement process. From these assumptions, Kalman filter can be designed to reduce variance of measurement noise. An optical and analysis system was set up, by which object motion with constant displacement and constant velocity is experimented with to verify validity of Kalman filtering techniques for reduction of measurement noise variance.

  14. AN ADAPTIVE OPTIMAL KALMAN FILTER FOR STOCHASTIC VIBRATION CONTROL SYSTEM WITH UNKNOWN NOISE VARIANCES

    Institute of Scientific and Technical Information of China (English)

    Li Shu; Zhuo Jiashou; Ren Qingwen

    2000-01-01

    In this paper, an optimal criterion is presented for adaptive Kalman filter in a control sys tem with unknown variances of stochastic vibration by constructing a function of noise variances and minimizing the function. We solve the model and measure variances by using DFP optimal method to guarantee the results of Kalman filter to be optimized. Finally, the control of vibration can be implemented by LQG method.

  15. Air-to-Air Missile Enhanced Scoring with Kalman Smoothing

    Science.gov (United States)

    2012-03-01

    Reconstruction Using The Unscented Kalman Filter Algorithm. Teixeira, Torres, Andrade de Oliveira, and Aguirre propose the use of an Un- scented Kalman...Paulo Henriques Iscold Andrade de Oliveira, and Luis Antonio Aguirre . “Flight Path Reconstruc- tion Using the Unscented Kalman Filter Algorithm

  16. Weighted Optimization-Based Distributed Kalman Filter for Nonlinear Target Tracking in Collaborative Sensor Networks.

    Science.gov (United States)

    Chen, Jie; Li, Jiahong; Yang, Shuanghua; Deng, Fang

    2016-07-21

    The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the "best" estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.

  17. The Application of Federated Kalman Filtering in SINS/GPS/CNS Intergrated Navigation System

    Directory of Open Access Journals (Sweden)

    DENG Chun-lin

    2012-04-01

    Full Text Available Federated filter was an important method to estimate high-precision navigation parameters based on “SINS/GPS/CNS”. A no-feedback federated filter with UD_UKF algorithm was designed in the paper, a threetime amendment scheme to correct navigation parameters was designed at the same time and the mathematical model of SINS/GPS/CNS was established in launch inertial coordinate system too. The paper discussed the simulation conditions and a lot of simulations were carried out to compare 2 aspects: (1the performance between four navigation mode, which respectively is SINS, SINS/GPS, SINS/CNS, SINS/GPS/CNS;(2the estimate precision of federated filter and that of centralized Kalman filter. The results of simulation showed that the designed federated filter and amendment scheme based on SINS/GPS/CNS had high estimate precision and led to gain high hitting precision of ballistic missile, that is to say position errors were less than 20 meter and velocity errors were less than 0.1m/s in simulation.

  18. Discrete Kalman Filter based Sensor Fusion for Robust Accessibility Interfaces

    Science.gov (United States)

    Ghersi, I.; Mariño, M.; Miralles, M. T.

    2016-04-01

    Human-machine interfaces have evolved, benefiting from the growing access to devices with superior, embedded signal-processing capabilities, as well as through new sensors that allow the estimation of movements and gestures, resulting in increasingly intuitive interfaces. In this context, sensor fusion for the estimation of the spatial orientation of body segments allows to achieve more robust solutions, overcoming specific disadvantages derived from the use of isolated sensors, such as the sensitivity of magnetic-field sensors to external influences, when used in uncontrolled environments. In this work, a method for the combination of image-processing data and angular-velocity registers from a 3D MEMS gyroscope, through a Discrete-time Kalman Filter, is proposed and deployed as an alternate user interface for mobile devices, in which an on-screen pointer is controlled with head movements. Results concerning general performance of the method are presented, as well as a comparative analysis, under a dedicated test application, with results from a previous version of this system, in which the relative-orientation information was acquired directly from MEMS sensors (3D magnetometer-accelerometer). These results show an improved response for this new version of the pointer, both in terms of precision and response time, while keeping many of the benefits that were highlighted for its predecessor, giving place to a complementary method for signal acquisition that can be used as an alternative-input device, as well as for accessibility solutions.

  19. Fuzzy Logic Based Autonomous Parallel Parking System with Kalman Filtering

    Science.gov (United States)

    Panomruttanarug, Benjamas; Higuchi, Kohji

    This paper presents an emulation of fuzzy logic control schemes for an autonomous parallel parking system in a backward maneuver. There are four infrared sensors sending the distance data to a microcontroller for generating an obstacle-free parking path. Two of them mounted on the front and rear wheels on the parking side are used as the inputs to the fuzzy rules to calculate a proper steering angle while backing. The other two attached to the front and rear ends serve for avoiding collision with other cars along the parking space. At the end of parking processes, the vehicle will be in line with other parked cars and positioned in the middle of the free space. Fuzzy rules are designed based upon a wall following process. Performance of the infrared sensors is improved using Kalman filtering. The design method needs extra information from ultrasonic sensors. Starting from modeling the ultrasonic sensor in 1-D state space forms, one makes use of the infrared sensor as a measurement to update the predicted values. Experimental results demonstrate the effectiveness of sensor improvement.

  20. IASI Radiance Data Assimilation in Local Ensemble Transform Kalman Filter

    Science.gov (United States)

    Cho, K.; Hyoung-Wook, C.; Jo, Y.

    2016-12-01

    Korea institute of Atmospheric Prediction Systems (KIAPS) is developing NWP model with data assimilation systems. Local Ensemble Transform Kalman Filter (LETKF) system, one of the data assimilation systems, has been developed for KIAPS Integrated Model (KIM) based on cubed-sphere grid and has successfully assimilated real data. LETKF data assimilation system has been extended to 4D- LETKF which considers time-evolving error covariance within assimilation window and IASI radiance data assimilation using KPOP (KIAPS package for observation processing) with RTTOV (Radiative Transfer for TOVS). The LETKF system is implementing semi operational prediction including conventional (sonde, aircraft) observation and AMSU-A (Advanced Microwave Sounding Unit-A) radiance data from April. Recently, the semi operational prediction system updated radiance observations including GPS-RO, AMV, IASI (Infrared Atmospheric Sounding Interferometer) data at July. A set of simulation of KIM with ne30np4 and 50 vertical levels (of top 0.3hPa) were carried out for short range forecast (10days) within semi operation prediction LETKF system with ensemble forecast 50 members. In order to only IASI impact, our experiments used only conventional and IAIS radiance data to same semi operational prediction set. We carried out sensitivity test for IAIS thinning method (3D and 4D). IASI observation number was increased by temporal (4D) thinning and the improvement of IASI radiance data impact on the forecast skill of model will expect.