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.

Ensemble Kalman filtering with one-step-ahead smoothing

KAUST Repository

Raboudi, Naila F.; Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim

2018-01-01

error statistics. This limits their representativeness of the background error covariances and, thus, their performance. This work explores the efficiency of the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to enhance

HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling.

Science.gov (United States)

Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee

2017-08-01

Epilepsy forecasting has been extensively studied using high-order time series obtained from scalp-recorded electroencephalography (EEG). An accurate seizure prediction system would not only help significantly improve patients' quality of life, but would also facilitate new therapeutic strategies to manage epilepsy. This paper thus proposes an improved Kalman Filter (KF) algorithm to mine seizure forecasts from neural activity by modeling three properties in the high-order EEG time series: noise, temporal smoothness, and tensor structure. The proposed High-Order Kalman Filter (HOKF) is an extension of the standard Kalman filter, for which higher-order modeling is limited. The efficient dynamic of HOKF system preserves the tensor structure of the observations and latent states. As such, the proposed method offers two main advantages: (i) effectiveness with HOKF results in hidden variables that capture major evolving trends suitable to predict neural activity, even in the presence of missing values; and (ii) scalability in that the wall clock time of the HOKF is linear with respect to the number of time-slices of the sequence. The HOKF algorithm is examined in terms of its effectiveness and scalability by conducting forecasting and scalability experiments with a real epilepsy EEG dataset. The results of the simulation demonstrate the superiority of the proposed method over the original Kalman Filter and other existing methods. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Use of the Kalman filter in signal processing to reduce beam requirements for alpha-particle diagnostics

International Nuclear Information System (INIS)

Cooper, W.S.

1986-01-01

Several techniques proposed for diagnosing the velocity distribution of fast alpha-particles in a burning plasma require the injection of a beam of fast neutral atoms as probes. The author discusses how improving signal detection techniques is a high leverage factor in reducing the cost of the diagnostic beam. Optimal estimation theory provides a computational algorithm, the Kalman filter, that can optimally estimate the amplitude of a signal with arbitrary (but known) time dependence in the presence of noise. In one example presented, based on a square-wave signal and assumed noise levels, the Kalman filter achieves an enhancement of signal detection efficiency of about a factor of 10 (as compared with the straightforward observation of the signal superimposed on noise) with an observation time of 100 signal periods

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.

Multilevel ensemble Kalman filtering

KAUST Repository

Hoel, Haakon; Chernov, Alexey; Law, Kody; Nobile, Fabio; Tempone, Raul

2016-01-01

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.

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.

An extended Kalman filter approach to non-stationary Bayesian estimation of reduced-order vocal fold model parameters.

Science.gov (United States)

Hadwin, Paul J; Peterson, Sean D

2017-04-01

The Bayesian framework for parameter inference provides a basis from which subject-specific reduced-order vocal fold models can be generated. Previously, it has been shown that a particle filter technique is capable of producing estimates and associated credibility intervals of time-varying reduced-order vocal fold model parameters. However, the particle filter approach is difficult to implement and has a high computational cost, which can be barriers to clinical adoption. This work presents an alternative estimation strategy based upon Kalman filtering aimed at reducing the computational cost of subject-specific model development. The robustness of this approach to Gaussian and non-Gaussian noise is discussed. The extended Kalman filter (EKF) approach is found to perform very well in comparison with the particle filter technique at dramatically lower computational cost. Based upon the test cases explored, the EKF is comparable in terms of accuracy to the particle filter technique when greater than 6000 particles are employed; if less particles are employed, the EKF actually performs better. For comparable levels of accuracy, the solution time is reduced by 2 orders of magnitude when employing the EKF. By virtue of the approximations used in the EKF, however, the credibility intervals tend to be slightly underpredicted.

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.

Carrier tracking by smoothing filter improves symbol SNR

Science.gov (United States)

Pomalaza-Raez, Carlos A.; Hurd, William J.

1986-01-01

The potential benefit of using a smoothing filter to estimate carrier phase over use of phase locked loops (PLL) is determined. Numerical results are presented for the performance of three possible configurations of the deep space network advanced receiver. These are residual carrier PLL, sideband aided residual carrier PLL, and finally sideband aiding with a Kalman smoother. The average symbol signal to noise ratio (SNR) after losses due to carrier phase estimation error is computed for different total power SNRs, symbol rates and symbol SNRs. It is found that smoothing is most beneficial for low symbol SNRs and low symbol rates. Smoothing gains up to 0.4 dB over a sideband aided residual carrier PLL, and the combined benefit of smoothing and sideband aiding relative to a residual carrier loop is often in excess of 1 dB.

Carrier tracking by smoothing filter can improve symbol SNR

Science.gov (United States)

Hurd, W. J.; Pomalaza-Raez, C. A.

1985-01-01

The potential benefit of using a smoothing filter to estimate carrier phase over use of phase locked loops (PLL) is determined. Numerical results are presented for the performance of three possible configurations of the deep space network advanced receiver. These are residual carrier PLL, sideband aided residual carrier PLL, and finally sideband aiding with a Kalman smoother. The average symbol signal to noise ratio (CNR) after losses due to carrier phase estimation error is computed for different total power SNRs, symbol rates and symbol SNRs. It is found that smoothing is most beneficial for low symbol SNRs and low symbol rates. Smoothing gains up to 0.4 dB over a sideband aided residual carrier PLL, and the combined benefit of smoothing and sideband aiding relative to a residual carrier loop is often in excess of 1 dB.

Dynamic Kalman filtering to separate low-frequency instabilities from turbulent fluctuations: Application to the Large-Eddy Simulation of unsteady turbulent flows

International Nuclear Information System (INIS)

Cahuzac, A; Boudet, J; Borgnat, P; Lévêque, E

2011-01-01

A dynamic method based on Kalman filtering is presented to isolate low-frequency unsteadiness from turbulent fluctuations in the large-eddy simulation (LES) of unsteady turbulent flows. The method can be viewed as an adaptive exponential smoothing, in which the smoothing factor adapts itself dynamically to the local behavior of the flow. Interestingly, the proposed method does not require any empirical tuning. In practice, it is used to estimate a shear-improved Smagorinsky viscosity, in which the low-frequency component of the velocity field is used to estimate a correction term to the Smagorinsky viscosity. The LES of the flow past a circular cylinder at Reynolds number Re D = 4.7 × 10 4 is examined as a challenging test case. Good comparisons are obtained with the experimental results, indicating the relevance of the shear-improved Smagorinsky model and the efficiency of the Kalman filtering. Finally, the adaptive cut-off of the Kalman filter is investigated, and shown to adapt locally and instantaneously to the complex flow around the cylinder.

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.

Kalman filter data assimilation: Targeting observations and parameter estimation

International Nuclear Information System (INIS)

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

2014-01-01

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

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.

An Iterative Ensemble Kalman Filter with One-Step-Ahead Smoothing for State-Parameters Estimation of Contaminant Transport Models

KAUST Repository

Gharamti, M. E.

2015-05-11

The ensemble Kalman filter (EnKF) is a popular method for state-parameters estimation of subsurface flow and transport models based on field measurements. The common filtering procedure is to directly update the state and parameters as one single vector, which is known as the Joint-EnKF. In this study, we follow the one-step-ahead smoothing formulation of the filtering problem, to derive a new joint-based EnKF which involves a smoothing step of the state between two successive analysis steps. The new state-parameters estimation scheme is derived in a consistent Bayesian filtering framework and results in separate update steps for the state and the parameters. This new algorithm bears strong resemblance with the Dual-EnKF, but unlike the latter which first propagates the state with the model then updates it with the new observation, the proposed scheme starts by an update step, followed by a model integration step. We exploit this new formulation of the joint filtering problem and propose an efficient model-integration-free iterative procedure on the update step of the parameters only for further improved performances. Numerical experiments are conducted with a two-dimensional synthetic subsurface transport model simulating the migration of a contaminant plume in a heterogenous aquifer domain. Contaminant concentration data are assimilated to estimate both the contaminant state and the hydraulic conductivity field. Assimilation runs are performed under imperfect modeling conditions and various observational scenarios. Simulation results suggest that the proposed scheme efficiently recovers both the contaminant state and the aquifer conductivity, providing more accurate estimates than the standard Joint and Dual EnKFs in all tested scenarios. Iterating on the update step of the new scheme further enhances the proposed filter’s behavior. In term of computational cost, the new Joint-EnKF is almost equivalent to that of the Dual-EnKF, but requires twice more model

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.

Particle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters

KAUST Repository

Hoteit, Ibrahim; Luo, Xiaodong; Pham, Dinh-Tuan; Moroz, Irene M.

2010-01-01

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.

A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models

KAUST Repository

El Gharamti, Mohamad; Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim

2015-01-01

The ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model’s state and parameters. These are generally estimated following a state-parameters joint augmentation

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.

Model-Based Engine Control Architecture with an Extended Kalman Filter

Science.gov (United States)

Csank, Jeffrey T.; Connolly, Joseph W.

2016-01-01

This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The nonlinear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.

Application of a Reduced Order Kalman Filter to Initialize a Coupled Atmosphere-Ocean Model: Impact on the Prediction of El Nino

Science.gov (United States)

Ballabrera-Poy, Joaquim; Busalacchi, Antonio J.; Murtugudde, Ragu

2000-01-01

A reduced order Kalman Filter, based on a simplification of the Singular Evolutive Extended Kalman (SEEK) filter equations, is used to assimilate observed fields of the surface wind stress, sea surface temperature and sea level into the nonlinear coupled ocean-atmosphere model. The SEEK filter projects the Kalman Filter equations onto a subspace defined by the eigenvalue decomposition of the error forecast matrix, allowing its application to high dimensional systems. The Zebiak and Cane model couples a linear reduced gravity ocean model with a single vertical mode atmospheric model of Zebiak. The compatibility between the simplified physics of the model and each observed variable is studied separately and together. The results show the ability of the model to represent the simultaneous value of the wind stress, SST and sea level, when the fields are limited to the latitude band 10 deg S - 10 deg N. In this first application of the Kalman Filter to a coupled ocean-atmosphere prediction model, the sea level fields are assimilated in terms of the Kelvin and Rossby modes of the thermocline depth anomaly. An estimation of the error of these modes is derived from the projection of an estimation of the sea level error over such modes. This method gives a value of 12 for the error of the Kelvin amplitude, and 6 m of error for the Rossby component of the thermocline depth. The ability of the method to reconstruct the state of the equatorial Pacific and predict its time evolution is demonstrated. The method is shown to be quite robust for predictions I up to six months, and able to predict the onset of the 1997 warm event fifteen months before its occurrence.

Simple smoothing technique to reduce data scattering in physics experiments

International Nuclear Information System (INIS)

Levesque, L

2008-01-01

This paper describes an experiment involving motorized motion and a method to reduce data scattering from data acquisition. Jitter or minute instrumental vibrations add noise to a detected signal, which often renders small modulations of a graph very difficult to interpret. Here we describe a method to reduce scattering amongst data points from the signal measured by a photodetector that is motorized and scanned in a direction parallel to the plane of a rectangular slit during a computer-controlled diffraction experiment. The smoothing technique is investigated using subsets of many data points from the data acquisition. A limit for the number of data points in a subset is determined from the results based on the trend of the small measured signal to avoid severe changes in the shape of the signal from the averaging procedure. This simple smoothing method can be achieved using any type of spreadsheet software

Deletion of Dicer in smooth muscle affects voiding pattern and reduces detrusor contractility and neuroeffector transmission.

Directory of Open Access Journals (Sweden)

Mardjaneh Karbalaei Sadegh

Full Text Available MicroRNAs have emerged as important regulators of smooth muscle phenotype and may play important roles in pathogenesis of various smooth muscle related disease states. The aim of this study was to investigate the role of miRNAs for urinary bladder function. We used an inducible and smooth muscle specific Dicer knockout (KO mouse which resulted in significantly reduced levels of miRNAs, including miR-145, miR-143, miR-22, miR125b-5p and miR-27a, from detrusor preparations without mucosa. Deletion of Dicer resulted in a disturbed micturition pattern in vivo and reduced depolarization-induced pressure development in the isolated detrusor. Furthermore, electrical field stimulation revealed a decreased cholinergic but maintained purinergic component of neurogenic activation in Dicer KO bladder strips. The ultrastructure of detrusor smooth muscle cells was well maintained, and the density of nerve terminals was similar. Western blotting demonstrated reduced contents of calponin and desmin. Smooth muscle α-actin, SM22α and myocardin were unchanged. Activation of strips with exogenous agonists showed that depolarization-induced contraction was preferentially reduced; ATP- and calyculin A-induced contractions were unchanged. Quantitative real time PCR and western blotting demonstrated reduced expression of Cav1.2 (Cacna1c. It is concluded that smooth muscle miRNAs play an important role for detrusor contractility and voiding pattern of unrestrained mice. This is mediated in part via effects on expression of smooth muscle differentiation markers and L-type Ca(2+ channels in the detrusor.

Kalman-fuzzy algorithm in short term load forecasting

International Nuclear Information System (INIS)

Shah Baki, S.R.; Saibon, H.; Lo, K.L.

1996-01-01

A combination of Kalman-Fuzzy-Neural is developed to forecast the next 24 hours load. The input data fed to neural network are presented with training data set composed of historical load data, weather, day of the week, month of the year and holidays. The load data is fed through Kalman-Fuzzy filter before being applied to Neural Network for training. With this techniques Neural Network converges faster and the mean percentage error of predicted load is reduced as compared to the classical ANN technique

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.

A new smoothing procedure to reduce delivery segments for static MLC-based IMRT planning

International Nuclear Information System (INIS)

Sun Xuepeng; Xia Ping

2004-01-01

In the application of pixel-based intensity-modulated radiation therapy (IMRT) using the step-and-shoot delivery method, one major difficulty is the prolonged delivery time. In this study, we present an integrated IMRT planning system that involves a simple smoothing method to reduce the complexity of the beam profiles. The system consists of three main steps: (a) an inverse planning process based on a least-square dose-based cost function; (b) smoothing of the intensity maps; (c) reoptimization of the segment weights. Step (a) obtains the best plan with the lowest cost value using a simulated annealing optimization algorithm with discrete intensity levels. Step (b) takes the intensity maps obtained from (a) and reduces the complexity of the maps by smoothing the adjacent beamlet intensities. During this process each beamlet is assigned a structure index based on anatomical information. A smoothing update is applied to average adjacent beamlets with the same index. To control the quality of the plan, a predefined clinical protocol is used as an acceptance criterion. The smoothing updates that violate the criterion are rejected. After the smoothing process, the segment weights are reoptimized in step (c) to further improve the plan quality. Three clinical cases were studied using this system: a medulloblastoma, a prostate cancer, and an oropharyngeal carcinoma. While the final plans demonstrate a degradation of the original plan quality, they still meet the plan acceptance criterion. On the other hand, the segment numbers or delivery times are reduced by 40%, 20%, and 20% for the three cases, respectively

Kalman filtering applied to a reagent feed system

International Nuclear Information System (INIS)

Griffin, C.D.; Croson, D.V.; Feeley, J.J.

1988-01-01

Using a Kalman filter solves a troublesome measurement noise problem and, at the same time, improves nuclear safety by detecting leaks to the process' feed tanks. To demonstrate how this technology of optimal estimation can be exploited, this article presents a systematic plan and example of how a Kalman filter was proven in industrial use on a reagent analyzer. A process to recycle uranium from spent fuel elements uses a reagent stream containing boron to dissolve the fuel. The boron is the neutron poison that prevents a nuclear chain reaction during the uranium dissolution. The purpose of the Kalman filter for this system is to reduce the uncertainty in the boron concentration measurement. The filter also provides incipient fault detection by estimating the unmeasured state of any unpoisoned solution, which would dilute the boron solution, entering the feed vessel

Using Kalman Filters to Reduce Noise from RFID Location System

Science.gov (United States)

Xavier, José; 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). PMID:24592186

A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part I: application in the Lorenz system

Directory of Open Access Journals (Sweden)

Lili Lei

2012-05-01

Full Text Available A hybrid data assimilation approach combining nudging and the ensemble Kalman filter (EnKF for dynamic analysis and numerical weather prediction is explored here using the non-linear Lorenz three-variable model system with the goal of a smooth, continuous and accurate data assimilation. The hybrid nudging-EnKF (HNEnKF computes the hybrid nudging coefficients from the flow-dependent, time-varying error covariance matrix from the EnKF's ensemble forecasts. It extends the standard diagonal nudging terms to additional off-diagonal statistical correlation terms for greater inter-variable influence of the innovations in the model's predictive equations to assist in the data assimilation process. The HNEnKF promotes a better fit of an analysis to data compared to that achieved by either nudging or incremental analysis update (IAU. When model error is introduced, it produces similar or better root mean square errors compared to the EnKF while minimising the error spikes/discontinuities created by the intermittent EnKF. It provides a continuous data assimilation with better inter-variable consistency and improved temporal smoothness than that of the EnKF. Data assimilation experiments are also compared to the ensemble Kalman smoother (EnKS. The HNEnKF has similar or better temporal smoothness than that of the EnKS, and with much smaller central processing unit (CPU time and data storage requirements.

A Method for Low-Delay Pitch Tracking and Smoothing

DEFF Research Database (Denmark)

Christensen, Mads Græsbøll

2012-01-01

. In the second step, a Kalman filter is used to smooth the estimates and separate the pitch into a slowly varying component and a rapidly varying component. The former represents the mean pitch while the latter represents vibrato, slides and other fast changes. The method is intended for use in applica- tions...... that require fast and sample-by-sample estimates, like tuners for musical instruments, transcription tasks requiring details like vi- brato, and real-time tracking of voiced speech....

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...

Accounting for Location Error in Kalman Filters: Integrating Animal Borne Sensor Data into Assimilation Schemes

Science.gov (United States)

Sengupta, Aritra; Foster, Scott D.; Patterson, Toby A.; Bravington, Mark

2012-01-01

Data assimilation is a crucial aspect of modern oceanography. It allows the future forecasting and backward smoothing of ocean state from the noisy observations. Statistical methods are employed to perform these tasks and are often based on or related to the Kalman filter. Typically Kalman filters assumes that the locations associated with observations are known with certainty. This is reasonable for typical oceanographic measurement methods. Recently, however an alternative and abundant source of data comes from the deployment of ocean sensors on marine animals. This source of data has some attractive properties: unlike traditional oceanographic collection platforms, it is relatively cheap to collect, plentiful, has multiple scientific uses and users, and samples areas of the ocean that are often difficult of costly to sample. However, inherent uncertainty in the location of the observations is a barrier to full utilisation of animal-borne sensor data in data-assimilation schemes. In this article we examine this issue and suggest a simple approximation to explicitly incorporate the location uncertainty, while staying in the scope of Kalman-filter-like methods. The approximation stems from a Taylor-series approximation to elements of the updating equation. PMID:22900005

随机奇异系统分布式最优融合降阶卡尔曼滤波器%Distributed Reduced-order Optimal Fusion Kalman Filters for Stochastic Singular Systems

Institute of Scientific and Technical Information of China (English)

孙书利; 马静

2006-01-01

Based on the optimal fusion algorithm weighted by matrices in the linear minimum variance (LMV) sense, a distributed full-order optimal fusion Kalman filter (DFFKF) is given for discrete-time stochastic singular systems with multiple sensors, which involves the inverse of a highdimension matrix to compute matrix weights. To reduce the computational burden, a distributed reduced-order fusion Kalman filter (DRFKF) is presented, which involves in parallel the inverses of two relatively low-dimension matrices to compute matrix weights. A simulation example shows the effectiveness.

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.

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.

The AGILE on-board Kalman filter

International Nuclear Information System (INIS)

Giuliani, A.; Cocco, V.; Mereghetti, S.; Pittori, C.; Tavani, M.

2006-01-01

On-board reduction of particle background is one of the main challenges of space instruments dedicated to gamma-ray astrophysics. We present in this paper a discussion of the method and main simulation results of the on-board background filter of the Gamma-Ray Imaging Detector (GRID) of the AGILE mission. The GRID is capable of detecting and imaging with optimal point spread function gamma-ray photons in the range 30MeV-30GeV. The AGILE planned orbit is equatorial, with an altitude of 550km. This is an optimal orbit from the point of view of the expected particle background. For this orbit, electrons and positrons of kinetic energies between 20MeV and hundreds of MeV dominate the particle background, with significant contributions from high-energy (primary) and low-energy protons, and gamma-ray albedo-photons. We present here the main results obtained by extensive simulations of the on-board AGILE-GRID particle/photon background rejection algorithms based on a special application of Kalman filter techniques. This filter is applied (Level-2) sequentially after other data processing techniques characterizing the Level-1 processing. We show that, in conjunction with the Level-1 processing, the adopted Kalman filtering is expected to reduce the total particle/albedo-photon background rate to a value (=<10-30Hz) that is compatible with the AGILE telemetry. The AGILE on-board Kalman filter is also effective in reducing the Earth-albedo-photon background rate, and therefore contributes to substantially increase the AGILE exposure for celestial gamma-ray sources

On the Kalman Filter error covariance collapse into the unstable subspace

Directory of Open Access Journals (Sweden)

A. Trevisan

2011-03-01

Full Text Available When the Extended Kalman Filter is applied to a chaotic system, the rank of the error covariance matrices, after a sufficiently large number of iterations, reduces to N⁺ + N⁰ where N⁺ and N⁰ are the number of positive and null Lyapunov exponents. This is due to the collapse into the unstable and neutral tangent subspace of the solution of the full Extended Kalman Filter. Therefore the solution is the same as the solution obtained by confining the assimilation to the space spanned by the Lyapunov vectors with non-negative Lyapunov exponents. Theoretical arguments and numerical verification are provided to show that the asymptotic state and covariance estimates of the full EKF and of its reduced form, with assimilation in the unstable and neutral subspace (EKF-AUS are the same. The consequences of these findings on applications of Kalman type Filters to chaotic models are discussed.

Multilevel ensemble Kalman filter

KAUST Repository

Chernov, Alexey; Hoel, Haakon; Law, Kody; Nobile, Fabio; Tempone, Raul

2016-01-01

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.

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.

Application of adaptive Kalman filter in vehicle laser Doppler velocimetry

Science.gov (United States)

Fan, Zhe; Sun, Qiao; Du, Lei; Bai, Jie; Liu, Jingyun

2018-03-01

Due to the variation of road conditions and motor characteristics of vehicle, great root-mean-square (rms) error and outliers would be caused. Application of Kalman filter in laser Doppler velocimetry(LDV) is important to improve the velocity measurement accuracy. In this paper, the state-space model is built by using current statistical model. A strategy containing two steps is adopted to make the filter adaptive and robust. First, the acceleration variance is adaptively adjusted by using the difference of predictive observation and measured observation. Second, the outliers would be identified and the measured noise variance would be adjusted according to the orthogonal property of innovation to reduce the impaction of outliers. The laboratory rotating table experiments show that adaptive Kalman filter greatly reduces the rms error from 0.59 cm/s to 0.22 cm/s and has eliminated all the outliers. Road experiments compared with a microwave radar show that the rms error of LDV is 0.0218 m/s, and it proves that the adaptive Kalman filtering is suitable for vehicle speed signal processing.

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.

Implementation of Kalman filter algorithm on models reduced using singular pertubation approximation method and its application to measurement of water level

Science.gov (United States)

Rachmawati, Vimala; Khusnul Arif, Didik; Adzkiya, Dieky

2018-03-01

The systems contained in the universe often have a large order. Thus, the mathematical model has many state variables that affect the computation time. In addition, generally not all variables are known, so estimations are needed to measure the magnitude of the system that cannot be measured directly. In this paper, we discuss the model reduction and estimation of state variables in the river system to measure the water level. The model reduction of a system is an approximation method of a system with a lower order without significant errors but has a dynamic behaviour that is similar to the original system. The Singular Perturbation Approximation method is one of the model reduction methods where all state variables of the equilibrium system are partitioned into fast and slow modes. Then, The Kalman filter algorithm is used to estimate state variables of stochastic dynamic systems where estimations are computed by predicting state variables based on system dynamics and measurement data. Kalman filters are used to estimate state variables in the original system and reduced system. Then, we compare the estimation results of the state and computational time between the original and reduced system.

Kalman Filtering and Smoothing of the Van Allen Probes Observations to Estimate the Radial, Energy and Pitch Angle Diffusion Rates

Science.gov (United States)

Podladchikova, T.; Shprits, Y.; Kellerman, A. C.

2015-12-01

The Kalman filter technique combines the strengths of new physical models of the Earth's radiation belts with long-term spacecraft observations of electron fluxes and therefore provide an extremely useful method for the analysis of the state and evolution of the electron radiation belts. However, to get the reliable data assimilation output, the Kalman filter application is confronted with a set of fundamental problems. E.g., satellite measurements are usually limited to a single location in space, which confines the reconstruction of the global evolution of the radiation environment. The uncertainties arise from the imperfect description of the process dynamics and the presence of observation errors, which may cause the failure of data assimilation solution. The development of adaptive Kalman filter that combines the Van Allen Probes data and 3-D VERB code, its accurate customizations in the reconstruction of model describing the phase space density (PSD) evolution, extension of the possibilities to use measurement information, and the model adjustment by developing the identification techniques of model and measurement errors allowed us to reveal hidden and implicit regularities of the PSD dynamics and obtain quantitative and qualitative estimates of radial, energy and pitch angle diffusion characteristics from satellite observations. In this study we propose an approach to estimate radial, energy and pitch angle diffusion rates, as well as the direction of their propagation.

A new deterministic Ensemble Kalman Filter with one-step-ahead smoothing for storm surge forecasting

KAUST Repository

Raboudi, Naila

2016-01-01

KF-OSA exploits the observation twice. The incoming observation is first used to smooth the ensemble at the previous time step. The resulting smoothed ensemble is then integrated forward to compute a "pseudo forecast" ensemble, which is again updated with the same

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.

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.

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......-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...

A generalized polynomial chaos based ensemble Kalman filter with high accuracy

International Nuclear Information System (INIS)

Li Jia; Xiu Dongbin

2009-01-01

As one of the most adopted sequential data assimilation methods in many areas, especially those involving complex nonlinear dynamics, the ensemble Kalman filter (EnKF) has been under extensive investigation regarding its properties and efficiency. Compared to other variants of the Kalman filter (KF), EnKF is straightforward to implement, as it employs random ensembles to represent solution states. This, however, introduces sampling errors that affect the accuracy of EnKF in a negative manner. Though sampling errors can be easily reduced by using a large number of samples, in practice this is undesirable as each ensemble member is a solution of the system of state equations and can be time consuming to compute for large-scale problems. In this paper we present an efficient EnKF implementation via generalized polynomial chaos (gPC) expansion. The key ingredients of the proposed approach involve (1) solving the system of stochastic state equations via the gPC methodology to gain efficiency; and (2) sampling the gPC approximation of the stochastic solution with an arbitrarily large number of samples, at virtually no additional computational cost, to drastically reduce the sampling errors. The resulting algorithm thus achieves a high accuracy at reduced computational cost, compared to the classical implementations of EnKF. Numerical examples are provided to verify the convergence property and accuracy improvement of the new algorithm. We also prove that for linear systems with Gaussian noise, the first-order gPC Kalman filter method is equivalent to the exact Kalman filter.

Mixtures of skewed Kalman filters

KAUST Repository

Kim, Hyoungmoon; Ryu, Duchwan; Mallick, Bani K.; Genton, Marc G.

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

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. Copyright © 2012 Wiley Periodicals, Inc.

The development rainfall forecasting using kalman filter

Science.gov (United States)

Zulfi, Mohammad; Hasan, Moh.; Dwidja Purnomo, Kosala

2018-04-01

Rainfall forecasting is very interesting for agricultural planing. Rainfall information is useful to make decisions about the plan planting certain commodities. In this studies, the rainfall forecasting by ARIMA and Kalman Filter method. Kalman Filter method is used to declare a time series model of which is shown in the form of linear state space to determine the future forecast. This method used a recursive solution to minimize error. The rainfall data in this research clustered by K-means clustering. Implementation of Kalman Filter method is for modelling and forecasting rainfall in each cluster. We used ARIMA (p,d,q) to construct a state space for KalmanFilter model. So, we have four group of the data and one model in each group. In conclusions, Kalman Filter method is better than ARIMA model for rainfall forecasting in each group. It can be showed from error of Kalman Filter method that smaller than error of ARIMA model.

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...... performs well when compared with the much more computationally intensive particle filter. These findings suggest that the unscented Kalman filter may be a good approach for a variety of problems in fixed-income pricing....

A coupling method for a cardiovascular simulation model which includes the Kalman filter.

Science.gov (United States)

Hasegawa, Yuki; Shimayoshi, Takao; Amano, Akira; Matsuda, Tetsuya

2012-01-01

Multi-scale models of the cardiovascular system provide new insight that was unavailable with in vivo and in vitro experiments. For the cardiovascular system, multi-scale simulations provide a valuable perspective in analyzing the interaction of three phenomenons occurring at different spatial scales: circulatory hemodynamics, ventricular structural dynamics, and myocardial excitation-contraction. In order to simulate these interactions, multiscale cardiovascular simulation systems couple models that simulate different phenomena. However, coupling methods require a significant amount of calculation, since a system of non-linear equations must be solved for each timestep. Therefore, we proposed a coupling method which decreases the amount of calculation by using the Kalman filter. In our method, the Kalman filter calculates approximations for the solution to the system of non-linear equations at each timestep. The approximations are then used as initial values for solving the system of non-linear equations. The proposed method decreases the number of iterations required by 94.0% compared to the conventional strong coupling method. When compared with a smoothing spline predictor, the proposed method required 49.4% fewer iterations.

A quantum extended Kalman filter

International Nuclear Information System (INIS)

Emzir, Muhammad F; Woolley, Matthew J; Petersen, Ian R

2017-01-01

In quantum physics, a stochastic master equation (SME) estimates the state (density operator) of a quantum system in the Schrödinger picture based on a record of measurements made on the system. In the Heisenberg picture, the SME is a quantum filter. For a linear quantum system subject to linear measurements and Gaussian noise, the dynamics may be described by quantum stochastic differential equations (QSDEs), also known as quantum Langevin equations, and the quantum filter reduces to a so-called quantum Kalman filter. In this article, we introduce a quantum extended Kalman filter (quantum EKF), which applies a commutative approximation and a time-varying linearization to systems of nonlinear QSDEs. We will show that there are conditions under which a filter similar to a classical EKF can be implemented for quantum systems. The boundedness of estimation errors and the filtering problem with ‘state-dependent’ covariances for process and measurement noises are also discussed. We demonstrate the effectiveness of the quantum EKF by applying it to systems that involve multiple modes, nonlinear Hamiltonians, and simultaneous jump-diffusive measurements. (paper)

A quantum extended Kalman filter

Science.gov (United States)

Emzir, Muhammad F.; Woolley, Matthew J.; Petersen, Ian R.

2017-06-01

In quantum physics, a stochastic master equation (SME) estimates the state (density operator) of a quantum system in the Schrödinger picture based on a record of measurements made on the system. In the Heisenberg picture, the SME is a quantum filter. For a linear quantum system subject to linear measurements and Gaussian noise, the dynamics may be described by quantum stochastic differential equations (QSDEs), also known as quantum Langevin equations, and the quantum filter reduces to a so-called quantum Kalman filter. In this article, we introduce a quantum extended Kalman filter (quantum EKF), which applies a commutative approximation and a time-varying linearization to systems of nonlinear QSDEs. We will show that there are conditions under which a filter similar to a classical EKF can be implemented for quantum systems. The boundedness of estimation errors and the filtering problem with ‘state-dependent’ covariances for process and measurement noises are also discussed. We demonstrate the effectiveness of the quantum EKF by applying it to systems that involve multiple modes, nonlinear Hamiltonians, and simultaneous jump-diffusive measurements.

Adaptive robust Kalman filtering for precise point positioning

International Nuclear Information System (INIS)

Guo, Fei; Zhang, Xiaohong

2014-01-01

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. (paper)

Gravity Matching Aided Inertial Navigation Technique Based on Marginal Robust Unscented Kalman Filter

Directory of Open Access Journals (Sweden)

Ming Liu

2015-01-01

Full Text Available This paper is concerned with the topic of gravity matching aided inertial navigation technology using Kalman filter. The dynamic state space model for Kalman filter is constructed as follows: the error equation of the inertial navigation system is employed as the process equation while the local gravity model based on 9-point surface interpolation is employed as the observation equation. The unscented Kalman filter is employed to address the nonlinearity of the observation equation. The filter is refined in two ways as follows. The marginalization technique is employed to explore the conditionally linear substructure to reduce the computational load; specifically, the number of the needed sigma points is reduced from 15 to 5 after this technique is used. A robust technique based on Chi-square test is employed to make the filter insensitive to the uncertainties in the above constructed observation model. Numerical simulation is carried out, and the efficacy of the proposed method is validated by the simulation results.

A Kalman Filter Implementation for Precision Improvement in Low-Cost GPS Positioning of Tractors

Science.gov (United States)

Gomez-Gil, Jaime; Ruiz-Gonzalez, Ruben; Alonso-Garcia, Sergio; Gomez-Gil, Francisco Javier

2013-01-01

Low-cost GPS receivers provide geodetic positioning information using the NMEA protocol, usually with eight digits for latitude and nine digits for longitude. When these geodetic coordinates are converted into Cartesian coordinates, the positions fit in a quantization grid of some decimeters in size, the dimensions of which vary depending on the point of the terrestrial surface. The aim of this study is to reduce the quantization errors of some low-cost GPS receivers by using a Kalman filter. Kinematic tractor model equations were employed to particularize the filter, which was tuned by applying Monte Carlo techniques to eighteen straight trajectories, to select the covariance matrices that produced the lowest Root Mean Square Error in these trajectories. Filter performance was tested by using straight tractor paths, which were either simulated or real trajectories acquired by a GPS receiver. The results show that the filter can reduce the quantization error in distance by around 43%. Moreover, it reduces the standard deviation of the heading by 75%. Data suggest that the proposed filter can satisfactorily preprocess the low-cost GPS receiver data when used in an assistance guidance GPS system for tractors. It could also be useful to smooth tractor GPS trajectories that are sharpened when the tractor moves over rough terrain. PMID:24217355

On Ensemble Nonlinear Kalman Filtering with Symmetric Analysis Ensembles

KAUST Repository

Luo, Xiaodong; Hoteit, Ibrahim; Moroz, Irene M.

2010-01-01

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].

Two-level Robust Measurement Fusion Kalman Filter for Clustering Sensor Networks

Institute of Scientific and Technical Information of China (English)

ZHANG Peng; QI Wen-Juan; DENG Zi-Li

2014-01-01

This paper investigates the distributed fusion Kalman filtering over clustering sensor networks. The sensor network is partitioned as clusters by the nearest neighbor rule and each cluster consists of sensing nodes and cluster-head. Using the minimax robust estimation principle, based on the worst-case conservative system with the conservative upper bounds of noise variances, two-level robust measurement fusion Kalman filter is presented for the clustering sensor network systems with uncertain noise variances. It can significantly reduce the communication load and save energy when the number of sensors is very large. A Lyapunov equation approach for the robustness analysis is presented, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented, and the robust accuracy relations among the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the two-level weighted measurement fuser is equal to that of the global centralized robust fuser and is higher than those of each local robust filter and each local weighted measurement fuser. A simulation example shows the correctness and effectiveness of the proposed results.

Adaptive Kalman Filter Applied to Vision Based Head Gesture Tracking for Playing Video Games

Directory of Open Access Journals (Sweden)

Mohammadreza Asghari Oskoei

2017-11-01

Full Text Available This paper proposes an adaptive Kalman filter (AKF to improve the performance of a vision-based human machine interface (HMI applied to a video game. The HMI identifies head gestures and decodes them into corresponding commands. Face detection and feature tracking algorithms are used to detect optical flow produced by head gestures. Such approaches often fail due to changes in head posture, occlusion and varying illumination. The adaptive Kalman filter is applied to estimate motion information and reduce the effect of missing frames in a real-time application. Failure in head gesture tracking eventually leads to malfunctioning game control, reducing the scores achieved, so the performance of the proposed vision-based HMI is examined using a game scoring mechanism. The experimental results show that the proposed interface has a good response time, and the adaptive Kalman filter improves the game scores by ten percent.

Real-time muscle state estimation from EMG signals during isometric contractions using Kalman filters.

Science.gov (United States)

Menegaldo, Luciano L

2017-12-01

State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators' amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations-OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17[Formula: see text] for eKF). The filtering lags presented sharp linear relationships with the AI (0-300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10-24% for activation, 5-8% for tendon force and 1.4-1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected.

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.

The Kalman Filter Revisited Using Maximum Relative Entropy

Directory of Open Access Journals (Sweden)

Adom Giffin

2014-02-01

Full Text Available In 1960, Rudolf E. Kalman created what is known as the Kalman filter, which is a way to estimate unknown variables from noisy measurements. The algorithm follows the logic that if the previous state of the system is known, it could be used as the best guess for the current state. This information is first applied a priori to any measurement by using it in the underlying dynamics of the system. Second, measurements of the unknown variables are taken. These two pieces of information are taken into account to determine the current state of the system. Bayesian inference is specifically designed to accommodate the problem of updating what we think of the world based on partial or uncertain information. In this paper, we present a derivation of the general Bayesian filter, then adapt it for Markov systems. A simple example is shown for pedagogical purposes. We also show that by using the Kalman assumptions or “constraints”, we can arrive at the Kalman filter using the method of maximum (relative entropy (MrE, which goes beyond Bayesian methods. Finally, we derive a generalized, nonlinear filter using MrE, where the original Kalman Filter is a special case. We further show that the variable relationship can be any function, and thus, approximations, such as the extended Kalman filter, the unscented Kalman filter and other Kalman variants are special cases as well.

MR fingerprinting reconstruction with Kalman filter.

Science.gov (United States)

Zhang, Xiaodi; Zhou, Zechen; Chen, Shiyang; Chen, Shuo; Li, Rui; Hu, Xiaoping

2017-09-01

Magnetic resonance fingerprinting (MR fingerprinting or MRF) is a newly introduced quantitative magnetic resonance imaging technique, which enables simultaneous multi-parameter mapping in a single acquisition with improved time efficiency. The current MRF reconstruction method is based on dictionary matching, which may be limited by the discrete and finite nature of the dictionary and the computational cost associated with dictionary construction, storage and matching. In this paper, we describe a reconstruction method based on Kalman filter for MRF, which avoids the use of dictionary to obtain continuous MR parameter measurements. With this Kalman filter framework, the Bloch equation of inversion-recovery balanced steady state free-precession (IR-bSSFP) MRF sequence was derived to predict signal evolution, and acquired signal was entered to update the prediction. The algorithm can gradually estimate the accurate MR parameters during the recursive calculation. Single pixel and numeric brain phantom simulation were implemented with Kalman filter and the results were compared with those from dictionary matching reconstruction algorithm to demonstrate the feasibility and assess the performance of Kalman filter algorithm. The results demonstrated that Kalman filter algorithm is applicable for MRF reconstruction, eliminating the need for a pre-define dictionary and obtaining continuous MR parameter in contrast to the dictionary matching algorithm. Copyright © 2017 Elsevier Inc. All rights reserved.

Square Root Unscented Kalman Filters for State Estimation of Induction Motor Drives

DEFF Research Database (Denmark)

Lascu, Cristian; Jafarzadeh, Saeed; Fadali, M.Sami

2013-01-01

This paper investigates the application, design, and implementation of the square root unscented Kalman filter (UKF) (SRUKF) for induction motor (IM) sensorless drives. The UKF uses nonlinear unscented transforms (UTs) in the prediction step in order to preserve the stochastic characteristics...... of a nonlinear system. The advantage of using the UT is its ability to capture the nonlinear behavior of the system, unlike the extended Kalman filter (EKF) that uses linearized models. The SRUKF implements the UKF using square root filtering to reduce computational errors. We discuss the theoretical aspects...

An adaptive Kalman filter for speckle reductions in ultrasound images

International Nuclear Information System (INIS)

Castellini, G.; Labate, D.; Masotti, L.; Mannini, E.; Rocchi, S.

1988-01-01

Speckle is the term used to describe the granular appearance found in ultrasound images. The presence of speckle reduces the diagnostic potential of the echographic technique because it tends to mask small inhomogeneities of the investigated tissue. We developed a new method of speckle reductions that utilizes an adaptive one-dimensional Kalman filter based on the assumption that the observed image can be considered as a superimposition of speckle on a ''true images''. The filter adaptivity, necessary to avoid loss of resolution, has been obtained by statistical considerations on the local signal variations. The results of the applications of this particular Kalman filter, both on A-Mode and B-MODE images, show a significant speckle reduction

Kalman filter-based gap conductance modeling

International Nuclear Information System (INIS)

Tylee, J.L.

1983-01-01

Geometric and thermal property uncertainties contribute greatly to the problem of determining conductance within the fuel-clad gas gap of a nuclear fuel pin. Accurate conductance values are needed for power plant licensing transient analysis and for test analyses at research facilities. Recent work by Meek, Doerner, and Adams has shown that use of Kalman filters to estimate gap conductance is a promising approach. A Kalman filter is simply a mathematical algorithm that employs available system measurements and assumed dynamic models to generate optimal system state vector estimates. This summary addresses another Kalman filter approach to gap conductance estimation and subsequent identification of an empirical conductance model

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.

A Two-stage Kalman Filter for Sensorless Direct Torque Controlled PM Synchronous Motor Drive

Directory of Open Access Journals (Sweden)

Boyu Yi

2013-01-01

Full Text Available This paper presents an optimal two-stage extended Kalman filter (OTSEKF for closed-loop flux, torque, and speed estimation of a permanent magnet synchronous motor (PMSM to achieve sensorless DTC-SVPWM operation of drive system. The novel observer is obtained by using the same transformation as in a linear Kalman observer, which is proposed by C.-S. Hsieh and F.-C. Chen in 1999. The OTSEKF is an effective implementation of the extended Kalman filter (EKF and provides a recursive optimum state estimation for PMSMs using terminal signals that may be polluted by noise. Compared to a conventional EKF, the OTSEKF reduces the number of arithmetic operations. Simulation and experimental results verify the effectiveness of the proposed OTSEKF observer for DTC of PMSMs.

A NEW METHOD OF CHANNEL FRICTION INVERSION BASED ON KALMAN FILTER WITH UNKNOWN PARAMETER VECTOR

Institute of Scientific and Technical Information of China (English)

CHENG Wei-ping; MAO Gen-hai; LIU Guo-hua

2005-01-01

Channel friction is an important parameter in hydraulic analysis.A channel friction parameter inversion method based on Kalman Filter with unknown parameter vector is proposed.Numerical simulations indicate that when the number of monitoring stations exceeds a critical value, the solution is hardly affected.In addition, Kalman Filter with unknown parameter vector is effective only at unsteady state.For the nonlinear equations, computations of sensitivity matrices are time-costly.Two simplified measures can reduce computing time, but not influence the results.One is to reduce sensitivity matrix analysis time, the other is to substitute for sensitivity matrix.

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......, which can be used to compute the Kalman filter gain....

Virtual microphone sensing through vibro-acoustic modelling and Kalman filtering

Science.gov (United States)

van de Walle, A.; Naets, F.; Desmet, W.

2018-05-01

This work proposes a virtual microphone methodology which enables full field acoustic measurements for vibro-acoustic systems. The methodology employs a Kalman filtering framework in order to combine a reduced high-fidelity vibro-acoustic model with a structural excitation measurement and small set of real microphone measurements on the system under investigation. By employing model order reduction techniques, a high order finite element model can be converted in a much smaller model which preserves the desired accuracy and maintains the main physical properties of the original model. Due to the low order of the reduced-order model, it can be effectively employed in a Kalman filter. The proposed methodology is validated experimentally on a strongly coupled vibro-acoustic system. The virtual sensor vastly improves the accuracy with respect to regular forward simulation. The virtual sensor also allows to recreate the full sound field of the system, which is very difficult/impossible to do through classical measurements.

Boundary Value Problems Arising in Kalman Filtering

Directory of Open Access Journals (Sweden)

Sinem Ertürk

2009-01-01

Full Text Available The classic Kalman filtering equations for independent and correlated white noises are ordinary differential equations (deterministic or stochastic with the respective initial conditions. Changing the noise processes by taking them to be more realistic wide band noises or delayed white noises creates challenging partial differential equations with initial and boundary conditions. In this paper, we are aimed to give a survey of this connection between Kalman filtering and boundary value problems, bringing them into the attention of mathematicians as well as engineers dealing with Kalman filtering and boundary value problems.

Boundary Value Problems Arising in Kalman Filtering

Directory of Open Access Journals (Sweden)

Bashirov Agamirza

2008-01-01

Full Text Available The classic Kalman filtering equations for independent and correlated white noises are ordinary differential equations (deterministic or stochastic with the respective initial conditions. Changing the noise processes by taking them to be more realistic wide band noises or delayed white noises creates challenging partial differential equations with initial and boundary conditions. In this paper, we are aimed to give a survey of this connection between Kalman filtering and boundary value problems, bringing them into the attention of mathematicians as well as engineers dealing with Kalman filtering and boundary value problems.

Multivariate localization methods for ensemble Kalman filtering

OpenAIRE

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

2015-01-01

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 ...

Effective wind speed estimation: Comparison between Kalman Filter and Takagi-Sugeno observer techniques.

Science.gov (United States)

Gauterin, Eckhard; Kammerer, Philipp; Kühn, Martin; Schulte, Horst

2016-05-01

Advanced model-based control of wind turbines requires knowledge of the states and the wind speed. This paper benchmarks a nonlinear Takagi-Sugeno observer for wind speed estimation with enhanced Kalman Filter techniques: The performance and robustness towards model-structure uncertainties of the Takagi-Sugeno observer, a Linear, Extended and Unscented Kalman Filter are assessed. Hence the Takagi-Sugeno observer and enhanced Kalman Filter techniques are compared based on reduced-order models of a reference wind turbine with different modelling details. The objective is the systematic comparison with different design assumptions and requirements and the numerical evaluation of the reconstruction quality of the wind speed. Exemplified by a feedforward loop employing the reconstructed wind speed, the benefit of wind speed estimation within wind turbine control is illustrated. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Pengenal Gerakan dengan Joystick Akselerometer Menggunakan Filter Kalman

Directory of Open Access Journals (Sweden)

Khoirudin Fathoni

2017-12-01

Full Text Available Human Machine Interaction keeps growing and developing, one of development is through gesture recognition that detects acceleration in a movement. This technology has been applied in joystick Wiimote and Wii-nunchuk by Nintendo that is widely used all over the world. Two main challenges in using accelerometer are to eliminate the noise of the sensor and to cancel the detected gravity acceleration when the joystick is tilted. The noise and gravity acceleration may influence the data reading and create error accumulation, respectively. This work proposes an implementation of Kalman Filter and also a simple technique to eliminate the influence of the gravity acceleration as a solution to solve above problems in using accelerometer of Wii-Nunchuk joystick in Board Arduino Mega 2560. The experimental results in motionless position show that the filter can reduce the gravity acceleration. We have to set the initial value of q and R parameters in the estimation of position, speed, and acceleration using Kalman filter. Once R is decided, the change of q will determine Kk gain, and it will locate the poles of the observer that influence the stability and the estimation result. With R=0.00005 and q=1, the poles of Kalman filter are located in the unit circle so that the estimation is stable and appropriate with the data from the sensor and even cancel the noise.

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.

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.

Adaptive probabilistic collocation based Kalman filter for unsaturated flow problem

Science.gov (United States)

Man, J.; Li, W.; Zeng, L.; Wu, L.

2015-12-01

The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a relatively large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the Polynomial Chaos to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so called "cure of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF is even more computationally expensive than EnKF. Motivated by recent developments in uncertainty quantification, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problem. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected. The "restart" technology is used to alleviate the inconsistency between model parameters and states. The performance of RAPCKF is tested by unsaturated flow numerical cases. It is shown that RAPCKF is more efficient than EnKF with the same computational cost. Compared with the traditional PCKF, the RAPCKF is more applicable in strongly nonlinear and high dimensional problems.

Concurrent hyperthermia estimation schemes based on extended Kalman filtering and reduced-order modelling.

Science.gov (United States)

Potocki, J K; Tharp, H S

1993-01-01

The success of treating cancerous tissue with heat depends on the temperature elevation, the amount of tissue elevated to that temperature, and the length of time that the tissue temperature is elevated. In clinical situations the temperature of most of the treated tissue volume is unknown, because only a small number of temperature sensors can be inserted into the tissue. A state space model based on a finite difference approximation of the bioheat transfer equation (BHTE) is developed for identification purposes. A full-order extended Kalman filter (EKF) is designed to estimate both the unknown blood perfusion parameters and the temperature at unmeasured locations. Two reduced-order estimators are designed as computationally less intensive alternatives to the full-order EKF. Simulation results show that the success of the estimation scheme depends strongly on the number and location of the temperature sensors. Superior results occur when a temperature sensor exists in each unknown blood perfusion zone, and the number of sensors is at least as large as the number of unknown perfusion zones. Unacceptable results occur when there are more unknown perfusion parameters than temperature sensors, or when the sensors are placed in locations that do not sample the unknown perfusion information.

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....

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...

Estimating Value at Risk with the Generalized Kalman Filter%基于Generalized Kalman Filter的VaR估计

Institute of Scientific and Technical Information of China (English)

赵利锋; 张崇岐

2009-01-01

在应用Kalman Filter方法估计时变风险β系数的基础上,引入Generalized Kalman Filter方法来估计时变卢系数,再通过Sharp对角线模型计算投资组合的VaR,并运用Backtesting检验判断两方法估计VaR的精确度.

Kalman Filtering for Delayed Singular Systems with Multiplicative Noise

Institute of Scientific and Technical Information of China (English)

Xiao Lu; Linglong Wang; Haixia Wang; Xianghua Wang

2016-01-01

Kalman filtering problem for singular systems is dealt with,where the measurements consist of instantaneous measurements and delayed ones,and the plant includes multiplicative noise.By utilizing standard singular value decomposition,the restricted equivalent delayed system is presented,and the Kalman filters for the restricted equivalent system are given by using the well-known re-organization of innovation analysis lemma.The optimal Kalman filter for the original system is given based on the above Kalman filter by recursive Riccati equations,and a numerical example is presented to show the validity and efficiency of the proposed approach,where the comparison between the filter and predictor is also given.

Kalman Filtering for Delayed Singular Systems with Multiplicative Noise

Institute of Scientific and Technical Information of China (English)

Xiao Lu; Linglong Wang; Haixia Wang; Xianghua Wang

2016-01-01

Kalman filtering problem for singular systems is dealt with, where the measurements consist of instantaneous measurements and delayed ones, and the plant includes multiplicative noise. By utilizing standard singular value decomposition, the restricted equivalent delayed system is presented, and the Kalman filters for the restricted equivalent system are given by using the well-known re-organization of innovation analysis lemma. The optimal Kalman filter for the original system is given based on the above Kalman filter by recursive Riccati equations, and a numerical example is presented to show the validity and efficiency of the proposed approach, where the comparison between the filter and predictor is also given.

A Self-Tuning Kalman Filter for Autonomous Navigation using the Global Positioning System (GPS)

Science.gov (United States)

Truong, S. H.

1999-01-01

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.

Robotic fish tracking method based on suboptimal interval Kalman filter

Science.gov (United States)

Tong, Xiaohong; Tang, Chao

2017-11-01

Autonomous Underwater Vehicle (AUV) research focused on tracking and positioning, precise guidance and return to dock and other fields. The robotic fish of AUV has become a hot application in intelligent education, civil and military etc. In nonlinear tracking analysis of robotic fish, which was found that the interval Kalman filter algorithm contains all possible filter results, but the range is wide, relatively conservative, and the interval data vector is uncertain before implementation. This paper proposes a ptimization algorithm of suboptimal interval Kalman filter. Suboptimal interval Kalman filter scheme used the interval inverse matrix with its worst inverse instead, is more approximate nonlinear state equation and measurement equation than the standard interval Kalman filter, increases the accuracy of the nominal dynamic system model, improves the speed and precision of tracking system. Monte-Carlo simulation results show that the optimal trajectory of sub optimal interval Kalman filter algorithm is better than that of the interval Kalman filter method and the standard method of the filter.

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model.

Science.gov (United States)

Xin, Jingzhou; Zhou, Jianting; Yang, Simon X; Li, Xiaoqing; Wang, Yu

2018-01-19

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model

Directory of Open Access Journals (Sweden)

Jingzhou Xin

2018-01-01

Full Text Available Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA, and generalized autoregressive conditional heteroskedasticity (GARCH. Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS deformation monitoring system demonstrated that: (1 the Kalman filter is capable of denoising the bridge deformation monitoring data; (2 the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3 in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity; the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data

Performance enhancement for a GPS vector-tracking loop utilizing an adaptive iterated extended Kalman filter.

Science.gov (United States)

Chen, Xiyuan; Wang, Xiying; Xu, Yuan

2014-12-09

This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively.

Ensemble Kalman methods for inverse problems

International Nuclear Information System (INIS)

Iglesias, Marco A; Law, Kody J H; Stuart, Andrew M

2013-01-01

The ensemble Kalman filter (EnKF) was introduced by Evensen in 1994 (Evensen 1994 J. Geophys. Res. 99 10143–62) as a novel method for data assimilation: state estimation for noisily observed time-dependent problems. Since that time it has had enormous impact in many application domains because of its robustness and ease of implementation, and numerical evidence of its accuracy. In this paper we propose the application of an iterative ensemble Kalman method for the solution of a wide class of inverse problems. In this context we show that the estimate of the unknown function that we obtain with the ensemble Kalman method lies in a subspace A spanned by the initial ensemble. Hence the resulting error may be bounded above by the error found from the best approximation in this subspace. We provide numerical experiments which compare the error incurred by the ensemble Kalman method for inverse problems with the error of the best approximation in A, and with variants on traditional least-squares approaches, restricted to the subspace A. In so doing we demonstrate that the ensemble Kalman method for inverse problems provides a derivative-free optimization method with comparable accuracy to that achieved by traditional least-squares approaches. Furthermore, we also demonstrate that the accuracy is of the same order of magnitude as that achieved by the best approximation. Three examples are used to demonstrate these assertions: inversion of a compact linear operator; inversion of piezometric head to determine hydraulic conductivity in a Darcy model of groundwater flow; and inversion of Eulerian velocity measurements at positive times to determine the initial condition in an incompressible fluid. (paper)

Enhancement of kalman filter single loss detection capability

International Nuclear Information System (INIS)

Morrison, G.W.; Downing, D.J.; Pike, D.H.

1980-01-01

A new technique to significantly increase the sensitivity of the Kalman filter to detect one-time losses for nuclear marterial accountability and control has been developed. The technique uses the innovations sequence obtained from a Kalman filter analysis of a material balance area. The innovations are distributed as zero mean independent Gaussion random variables with known variance. This property enables an estimator to be formed with enhanced one time loss detection capabilities. Simulation studies of a material balance area indicate the new estimator greatly enhances the one time loss detection capability of the Kalman filter

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.

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.

Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm

Science.gov (United States)

Song, Huijie; Dong, Shaowu; Wu, Wenjun; Jiang, Meng; Wang, Weixiong

2018-06-01

The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’ the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test.

Kalman filter to update forest cover estimates

Science.gov (United States)

Raymond L. Czaplewski

1990-01-01

The Kalman filter is a statistical estimator that combines a time-series of independent estimates, using a prediction model that describes expected changes in the state of a system over time. An expensive inventory can be updated using model predictions that are adjusted with more recent, but less expensive and precise, monitoring data. The concepts of the Kalman...

Kalman-predictive-proportional-integral-derivative (KPPID)

International Nuclear Information System (INIS)

Fluerasu, A.; Sutton, M.

2004-01-01

With third generation synchrotron X-ray sources, it is possible to acquire detailed structural information about the system under study with time resolution orders of magnitude faster than was possible a few years ago. These advances have generated many new challenges for changing and controlling the state of the system on very short time scales, in a uniform and controlled manner. For our particular X-ray experiments on crystallization or order-disorder phase transitions in metallic alloys, we need to change the sample temperature by hundreds of degrees as fast as possible while avoiding over or under shooting. To achieve this, we designed and implemented a computer-controlled temperature tracking system which combines standard Proportional-Integral-Derivative (PID) feedback, thermal modeling and finite difference thermal calculations (feedforward), and Kalman filtering of the temperature readings in order to reduce the noise. The resulting Kalman-Predictive-Proportional-Integral-Derivative (KPPID) algorithm allows us to obtain accurate control, to minimize the response time and to avoid over/under shooting, even in systems with inherently noisy temperature readings and time delays. The KPPID temperature controller was successfully implemented at the Advanced Photon Source at Argonne National Laboratories and was used to perform coherent and time-resolved X-ray diffraction experiments.

Mixed-Degree Spherical Simplex-Radial Cubature Kalman Filter

Directory of Open Access Journals (Sweden)

Shiyuan Wang

2017-01-01

Full Text Available Conventional low degree spherical simplex-radial cubature Kalman filters often generate low filtering accuracy or even diverge for handling highly nonlinear systems. The high-degree Kalman filters can improve filtering accuracy at the cost of increasing computational complexity; nevertheless their stability will be influenced by the negative weights existing in the high-dimensional systems. To efficiently improve filtering accuracy and stability, a novel mixed-degree spherical simplex-radial cubature Kalman filter (MSSRCKF is proposed in this paper. The accuracy analysis shows that the true posterior mean and covariance calculated by the proposed MSSRCKF can agree accurately with the third-order moment and the second-order moment, respectively. Simulation results show that, in comparison with the conventional spherical simplex-radial cubature Kalman filters that are based on the same degrees, the proposed MSSRCKF can perform superior results from the aspects of filtering accuracy and computational complexity.

Kalman Filter Predictor and Initialization Algorithm for PRI Tracking

National Research Council Canada - National Science Library

Hock, Melinda

1998-01-01

.... The algorithm uses a Kalman filter for prediction combined with a preprocessing routine to determine the period of the stagger sequence and to construct an uncorrupted data set for Kalman filter initialization...

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...... processing and so on. This paper provides a brief overview of the industrial applications and implementation issues of the Kalman filter in six topics of the industrial electronics community, highlighting some relevant reference papers and giving future research trends....

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.

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…

Robust cubature Kalman filter for GNSS/INS with missing observations and colored measurement noise.

Science.gov (United States)

Cui, Bingbo; Chen, Xiyuan; Tang, Xihua; Huang, Haoqian; Liu, Xiao

2018-01-01

In order to improve the accuracy of GNSS/INS working in GNSS-denied environment, a robust cubature Kalman filter (RCKF) is developed by considering colored measurement noise and missing observations. First, an improved cubature Kalman filter (CKF) is derived by considering colored measurement noise, where the time-differencing approach is applied to yield new observations. Then, after analyzing the disadvantages of existing methods, the measurement augment in processing colored noise is translated into processing the uncertainties of CKF, and new sigma point update framework is utilized to account for the bounded model uncertainties. By reusing the diffused sigma points and approximation residual in the prediction stage of CKF, the RCKF is developed and its error performance is analyzed theoretically. Results of numerical experiment and field test reveal that RCKF is more robust than CKF and extended Kalman filter (EKF), and compared with EKF, the heading error of land vehicle is reduced by about 72.4%. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

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.

On tempo tracking: Tempogram representation and Kalman filtering

NARCIS (Netherlands)

Cemgil, A.T.; Kappen, H.J.; Desain, P.W.M.; Honing, H.J.

2001-01-01

We formulate tempo tracking in a Bayesian framework where a tempo tracker is modeled as a stochastic dynamical system. The tempo is modeled as a hidden state variable of the system and is estimated by a Kalman filter. The Kalman filter operates on a Tempogram, a wavelet-like multiscale expansion of

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.

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

2014-01-01

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. PMID:25479331

Kalman filter estimation of RLC parameters for UMP transmission line

Directory of Open Access Journals (Sweden)

Mohd Amin Siti Nur Aishah

2018-01-01

Full Text Available This paper present the development of Kalman filter that allows evaluation in the estimation of resistance (R, inductance (L, and capacitance (C values for Universiti Malaysia Pahang (UMP short transmission line. To overcome the weaknesses of existing system such as power losses in the transmission line, Kalman Filter can be a better solution to estimate the parameters. The aim of this paper is to estimate RLC values by using Kalman filter that in the end can increase the system efficiency in UMP. In this research, matlab simulink model is developed to analyse the UMP short transmission line by considering different noise conditions to reprint certain unknown parameters which are difficult to predict. The data is then used for comparison purposes between calculated and estimated values. The results have illustrated that the Kalman Filter estimate accurately the RLC parameters with less error. The comparison of accuracy between Kalman Filter and Least Square method is also presented to evaluate their performances.

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.

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.

Estimation of Sideslip Angle Based on Extended Kalman Filter

Directory of Open Access Journals (Sweden)

Yupeng Huang

2017-01-01

Full Text Available The sideslip angle plays an extremely important role in vehicle stability control, but the sideslip angle in production car cannot be obtained from sensor directly in consideration of the cost of the sensor; it is essential to estimate the sideslip angle indirectly by means of other vehicle motion parameters; therefore, an estimation algorithm with real-time performance and accuracy is critical. Traditional estimation method based on Kalman filter algorithm is correct in vehicle linear control area; however, on low adhesion road, vehicles have obvious nonlinear characteristics. In this paper, extended Kalman filtering algorithm had been put forward in consideration of the nonlinear characteristic of the tire and was verified by the Carsim and Simulink joint simulation, such as the simulation on the wet cement road and the ice and snow road with double lane change. To test and verify the effect of extended Kalman filtering estimation algorithm, the real vehicle test was carried out on the limit test field. The experimental results show that the accuracy of vehicle sideslip angle acquired by extended Kalman filtering algorithm is obviously higher than that acquired by Kalman filtering in the area of the nonlinearity.

Improving Artificial eural etwork Forecasts with Kalman Filtering

African Journals Online (AJOL)

Nafiisah

technique in financial time series and the application of a Kalman filter ... networks (ANN) model using a Kalman filter leads to significant improvements in .... 3-rd order polynomial (Galanis et al. (2006)): 1 t p. 2 t p. 3 t p. 4 t p. 1 t h. 2 t h tr t r ...

Design of Kalman filters for mobile robots

DEFF Research Database (Denmark)

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

1999-01-01

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......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...... estimates. The Kalman filter normally consists of a time update followed by one or more data updates. However, it is shown that when using the kinematic filter, the encoder measurements should be fused prior to the time update for better performance....

An adaptive Multiplicative Extened Kalman Filter for Attitude Estimation of Marine Satellite Tracking Antenna

DEFF Research Database (Denmark)

Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar

2016-01-01

, an adaptive Multiplicative Extended Kalman Filter (MEKF) for attitude estimation of Marine Satellite Tracking Antenna (MSTA) is presented with the measurement noise covariance matrix adjusted according to the norm of accelerometer measurements, which can significantly reduce the slamming influence from waves...

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).

Bias aware Kalman filters

DEFF Research Database (Denmark)

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

2006-01-01

This paper reviews two different approaches that have been proposed to tackle the problems of model bias with the Kalman filter: the use of a colored noise model and the implementation of a separate bias filter. Both filters are implemented with and without feedback of the bias into the model state....... 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....

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].

Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation

Science.gov (United States)

Simon, Donald L.; Garg, Sanjay

2011-01-01

An emerging approach in the field of aircraft engine controls and system health management is the inclusion of real-time, onboard models for the inflight estimation of engine performance variations. This technology, typically based on Kalman-filter concepts, enables the estimation of unmeasured engine performance parameters that can be directly utilized by controls, prognostics, and health-management applications. A challenge that complicates this practice is the fact that an aircraft engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. Through Kalman-filter-based estimation techniques, the level of engine performance degradation can be estimated, given that there are at least as many sensors as health parameters to be estimated. However, in an aircraft engine, the number of sensors available is typically less than the number of health parameters, presenting an under-determined estimation problem. A common approach to address this shortcoming is to estimate a subset of the health parameters, referred to as model tuning parameters. The problem/objective is to optimally select the model tuning parameters to minimize Kalman-filterbased estimation error. A tuner selection technique has been developed that specifically addresses the under-determined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine that seeks to minimize the theoretical mean-squared estimation error of the Kalman filter. This approach can significantly reduce the error in onboard aircraft engine parameter estimation

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...... and estimating parameters requires a much larger ensemble size than just assimilating groundwater head observations. However, the required ensemble size can be greatly reduced with the use of adaptive localization, which by far outperforms distance-based localization. The study is conducted using synthetic data...

LHCb Kalman Filter cross architecture studies

Science.gov (United States)

Hugo, Daniel; Pérez, Cámpora

2017-10-01

The 2020 upgrade of the LHCb detector will vastly increase the rate of collisions the Online system needs to process in software, in order to filter events in real time. 30 million collisions per second will pass through a selection chain, where each step is executed conditional to its prior acceptance. The Kalman Filter is a fit applied to all reconstructed tracks which, due to its time characteristics and early execution in the selection chain, consumes 40% of the whole reconstruction time in the current trigger software. This makes the Kalman Filter a time-critical component as the LHCb trigger evolves into a full software trigger in the Upgrade. I present a new Kalman Filter algorithm for LHCb that can efficiently make use of any kind of SIMD processor, and its design is explained in depth. Performance benchmarks are compared between a variety of hardware architectures, including x86_64 and Power8, and the Intel Xeon Phi accelerator, and the suitability of said architectures to efficiently perform the LHCb Reconstruction process is determined.

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

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.

A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation

KAUST Repository

Altaf, Muhammad; Butler, T.; Mayo, T.; Luo, X.; Dawson, C.; Heemink, A. W.; Hoteit, Ibrahim

2014-01-01

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.

Estimating model error covariances in nonlinear state-space models using Kalman smoothing and the expectation-maximisation algorithm

KAUST Repository

Dreano, Denis

2017-04-05

Specification and tuning of errors from dynamical models are important issues in data assimilation. In this work, we propose an iterative expectation-maximisation (EM) algorithm to estimate the model error covariances using classical extended and ensemble versions of the Kalman smoother. We show that, for additive model errors, the estimate of the error covariance converges. We also investigate other forms of model error, such as parametric or multiplicative errors. We show that additive Gaussian model error is able to compensate for non additive sources of error in the algorithms we propose. We also demonstrate the limitations of the extended version of the algorithm and recommend the use of the more robust and flexible ensemble version. This article is a proof of concept of the methodology with the Lorenz-63 attractor. We developed an open-source Python library to enable future users to apply the algorithm to their own nonlinear dynamical models.

Kalman-Takens filtering in the presence of dynamical noise

Science.gov (United States)

Hamilton, Franz; Berry, Tyrus; Sauer, Timothy

2017-12-01

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. In this article, we study the efficacy of this method for identifying underlying dynamics in the presence of dynamical noise. Furthermore, 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 when no dynamical model is specified.

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.

Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise

Directory of Open Access Journals (Sweden)

Bowen Hou

2017-11-01

Full Text Available As one of the most critical issues for target track, α -jerk model is an effective maneuver target track model. Non-Gaussian noises always exist in the track process, which usually lead to inconsistency and divergence of the track filter. A novel Kalman filter is derived and applied on α -jerk tracking model to handle non-Gaussian noise. The weighted least square solution is presented and the standard Kalman filter is deduced firstly. A novel Kalman filter with the weighted least square based on the maximum correntropy criterion is deduced. The robustness of the maximum correntropy criterion is also analyzed with the influence function and compared with the Huber-based filter, and, moreover, the kernel size of Gaussian kernel plays an important role in the filter algorithm. A new adaptive kernel method is proposed in this paper to adjust the parameter in real time. Finally, simulation results indicate the validity and the efficiency of the proposed filter. The comparison study shows that the proposed filter can significantly reduce the noise influence for α -jerk model.

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.

Kalman filters for real-time magnetic island phase tracking

International Nuclear Information System (INIS)

Borgers, D.P.; Lauret, M.; Baar, M.R. de

2013-01-01

Highlights: • We propose two Kalman filters for tracking of NTMs on ASDEX Upgrade. • The Kalman filters can track NTMs in a much larger frequency range than PLLs. • The filters are tested on synthetic and experimental data from TEXTOR and TCV. • We conclude that the unscented Kalman filter can be useful for NTM control. -- Abstract: For control of neoclassical tearing modes (NTMs) and the resulting rotating magnetic islands in tokamak plasmas, the frequency and phase of the magnetic islands need to be accurately tracked in real-time. In previous experiments on TEXTOR, this was achieved using a phase-locked loop (PLL). For ASDEX Upgrade however, the desired frequency range in which the islands are to be tracked (100 Hz–10 kHz) is much larger than is possible with a PLL. In this contribution, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF) are proposed for real-time frequency, phase and amplitude tracking of sinusoidal signals, based on noisy measurements. Compared to PLLs, the EKF and UKF are able to track sinusoidal signals in a much larger frequency range. The filters are applied on synthetic data and on experimental data from the TEXTOR and TCV tokamaks, from which we conclude that the UKF can be useful for real-time control of magnetic islands on ASDEX Upgrade

Kalman filters for real-time magnetic island phase tracking

Energy Technology Data Exchange (ETDEWEB)

Borgers, D.P. [Hybrid and Networked Systems, Department of Mechanical Engineering – Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven (Netherlands); Lauret, M., E-mail: M.Lauret@tue.nl [FOM Institute DIFFER – Dutch Institute for Fundamental Energy Research, Association EURATOM-FOM, Trilateral Euregio Cluster, P.O. Box 1207, Nieuwegein (Netherlands); Control Systems Technology, Department of Mechanical Engineering – Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven (Netherlands); Baar, M.R. de [FOM Institute DIFFER – Dutch Institute for Fundamental Energy Research, Association EURATOM-FOM, Trilateral Euregio Cluster, P.O. Box 1207, Nieuwegein (Netherlands); Control Systems Technology, Department of Mechanical Engineering – Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven (Netherlands)

2013-11-15

Highlights: • We propose two Kalman filters for tracking of NTMs on ASDEX Upgrade. • The Kalman filters can track NTMs in a much larger frequency range than PLLs. • The filters are tested on synthetic and experimental data from TEXTOR and TCV. • We conclude that the unscented Kalman filter can be useful for NTM control. -- Abstract: For control of neoclassical tearing modes (NTMs) and the resulting rotating magnetic islands in tokamak plasmas, the frequency and phase of the magnetic islands need to be accurately tracked in real-time. In previous experiments on TEXTOR, this was achieved using a phase-locked loop (PLL). For ASDEX Upgrade however, the desired frequency range in which the islands are to be tracked (100 Hz–10 kHz) is much larger than is possible with a PLL. In this contribution, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF) are proposed for real-time frequency, phase and amplitude tracking of sinusoidal signals, based on noisy measurements. Compared to PLLs, the EKF and UKF are able to track sinusoidal signals in a much larger frequency range. The filters are applied on synthetic data and on experimental data from the TEXTOR and TCV tokamaks, from which we conclude that the UKF can be useful for real-time control of magnetic islands on ASDEX Upgrade.

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.

Reservoir History Matching Using Ensemble Kalman Filters with Anamorphosis Transforms

KAUST Repository

Aman, Beshir M.

2012-01-01

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

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.

Harmonic Detection at Initialization With Kalman Filter

DEFF Research Database (Denmark)

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

2014-01-01

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...... 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...

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.

An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors.

Science.gov (United States)

Foussier, Jerome; Teichmann, Daniel; Jia, Jing; Misgeld, Berno; Leonhardt, Steffen

2014-05-09

Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min(-1) (0.3 min(-1)) and -0.7 bpm (1.7 bpm) (compared to -0.2 min(-1) (0.4 min(-1)) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed

Robust Sequential Covariance Intersection Fusion Kalman Filtering over Multi-agent Sensor Networks with Measurement Delays and Uncertain Noise Variances

Institute of Scientific and Technical Information of China (English)

QI Wen-Juan; ZHANG Peng; DENG Zi-Li

2014-01-01

This paper deals with the problem of designing robust sequential covariance intersection (SCI) fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise variances. The sensor network is partitioned into clusters by the nearest neighbor rule. Using the minimax robust estimation principle, based on the worst-case conservative sensor network system with conservative upper bounds of noise variances, and applying the unbiased linear minimum variance (ULMV) optimal estimation rule, we present the two-layer SCI fusion robust steady-state Kalman filter which can reduce communication and computation burdens and save energy sources, and guarantee that the actual filtering error variances have a less-conservative upper-bound. A Lyapunov equation method for robustness analysis is proposed, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented and the robust accuracy relations of the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the global SCI fuser is higher than those of the local SCI fusers and the robust accuracies of all SCI fusers are higher than that of each local robust Kalman filter. A simulation example for a tracking system verifies the robustness and robust accuracy relations.

A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment

Directory of Open Access Journals (Sweden)

Zhu Xiao

2016-05-01

Full Text Available In this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS, is proposed, which enables vehicle state estimation (VSE with high accuracy taking into account the non-Gaussianity of the measurement and process noises. Within the proposed method, the multivariate Student’s t-distribution is adopted in order to compute the probability distribution function (PDF related to the process and measurement noises, which are assumed to be non-Gaussian distributed. A computation approach based on Ensemble Kalman Filter (EnKF is designed to cope with the mean and the covariance matrix of the proposal non-Gaussian distribution. A delayed Gibbs sampling algorithm, which incorporates smoothing of the sampled trajectories over a fixed-delay, is proposed to deal with the sample degeneracy of particles. The performance is investigated based on the real-world data, which is collected by low-cost on-board vehicle sensors. The comparison study based on the real-world experiments and the statistical analysis demonstrates that the proposed nGDPS has significant improvement on the vehicle state accuracy and outperforms the existing filtering and smoothing methods.

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

Directory of Open Access Journals (Sweden)

Haoqian Huang

2014-12-01

Full Text Available 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.

Comparison of several Kalman filter models for establishing MUF

International Nuclear Information System (INIS)

Pike, D.H.; Morrison, G.W.; Holland, C.W.

1976-01-01

Detection of MUF in a material balance area is a problem in nuclear material control. It has been shown that the Kalman filter can detect a MUF in situations which could not be detected by the traditional control chart approach using LEMUF. The Kalman filter is extended in this paper to cover two additional scenarios: (1) the case where a random quantity with a mean of M(t) is removed per period, and (2) the case where MUF is a fraction of the on-hand inventory each period. The Kalman filter is robust, sensitive, produces estimates of the error covariance matrix, and is an iterative technique which is suited for on-line-direct-input information systems

An improved fuzzy Kalman filter for state estimation of nonlinear systems

International Nuclear Information System (INIS)

Zhou, Z-J; Hu, C-H; Chen, L; Zhang, B-C

2008-01-01

The extended fuzzy Kalman filter (EFKF) is developed recently and used for state estimation of the nonlinear systems with uncertainty. Based on extension of the orthogonality principle and the extended fuzzy Kalman filter, an improved fuzzy Kalman filters (IFKF) is proposed in this paper, which is more applicable and can deal with the state estimation of the nonlinear systems better than the EFKF. A simulation study is provided to verify the efficiency of the proposed method

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

Free Space Computation From Stochastic Occupancy Grids Based On Iconic Kalman Filtered Disparity Maps

DEFF Research Database (Denmark)

Høilund, Carsten; Moeslund, Thomas B.; Madsen, Claus B.

2010-01-01

This paper presents a method for determining the free space in a scene as viewed by a vehicle-mounted camera. Using disparity maps from a stereo camera and known camera motion, the disparity maps are first filtered by an iconic Kalman filter, operating on each pixel individually, thereby reducing...

Data assimilation in the early phase: Kalman filtering RIMPUFF

International Nuclear Information System (INIS)

Astrup, P.; Turcanu, C.; Puch, R.O.; Palma, C.R.; Mikkelsen, T.

2004-09-01

In the framework of the DAONEM project (Data Assimilation for Off-site Nuclear Emergency Management), a data assimilation module, ADUM (Atmospheric Dispersion Updating Module), for the mesoscale atmospheric dispersion program RIMPUFF (Risoe Mesoscale Puff model) part of the early-phase programs 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 at ground 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 vector variables 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 dense distribution 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 module, ToDeMM, has been developed for providing the late-phase DeMM (Deposition Monitoring Module) of RODOS with an ensemble of fields of ground level air concentrations and wet deposited material. This accounts for the uncertainty estimation of this kind of quantities as calculated by RIMPUFF for use by DeMM. (au)

Smoothness in Binomial Edge Ideals

Directory of Open Access Journals (Sweden)

Hamid Damadi

2016-06-01

Full Text Available In this paper we study some geometric properties of the algebraic set associated to the binomial edge ideal of a graph. We study the singularity and smoothness of the algebraic set associated to the binomial edge ideal of a graph. Some of these algebraic sets are irreducible and some of them are reducible. If every irreducible component of the algebraic set is smooth we call the graph an edge smooth graph, otherwise it is called an edge singular graph. We show that complete graphs are edge smooth and introduce two conditions such that the graph G is edge singular if and only if it satisfies these conditions. Then, it is shown that cycles and most of trees are edge singular. In addition, it is proved that complete bipartite graphs are edge smooth.

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.

Extracting Steady State Components from Synchrophasor Data Using Kalman Filters

Directory of Open Access Journals (Sweden)

Farhan Mahmood

2016-04-01

Full Text Available Data from phasor measurement units (PMUs may be exploited to provide steady state information to the applications which require it. As PMU measurements may contain errors and missing data, the paper presents the application of a Kalman Filter technique for real-time data processing. PMU data captures the power system’s response at different time-scales, which are generated by different types of power system events; the presented Kalman Filter methods have been applied to extract the steady state components of PMU measurements that can be fed to steady state applications. Two KF-based methods have been proposed, i.e., a windowing-based KF method and “the modified KF”. Both methods are capable of reducing noise, compensating for missing data and filtering outliers from input PMU signals. A comparison of proposed methods has been carried out using the PMU data generated from a hardware-in-the-loop (HIL experimental setup. In addition, a performance analysis of the proposed methods is performed using an evaluation metric.

Estimation of three-dimensional radar tracking using modified extended kalman filter

Science.gov (United States)

Aditya, Prima; Apriliani, Erna; Khusnul Arif, Didik; Baihaqi, Komar

2018-03-01

Kalman filter is an estimation method by combining data and mathematical models then developed be extended Kalman filter to handle nonlinear systems. Three-dimensional radar tracking is one of example of nonlinear system. In this paper developed a modification method of extended Kalman filter from the direct decline of the three-dimensional radar tracking case. The development of this filter algorithm can solve the three-dimensional radar measurements in the case proposed in this case the target measured by radar with distance r, azimuth angle θ, and the elevation angle ϕ. Artificial covariance and mean adjusted directly on the three-dimensional radar system. Simulations result show that the proposed formulation is effective in the calculation of nonlinear measurement compared with extended Kalman filter with the value error at 0.77% until 1.15%.

Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter

Energy Technology Data Exchange (ETDEWEB)

Stetzel, KD; Aldrich, LL; Trimboli, MS; Plett, GL

2015-03-15

This paper addresses the problem of estimating the present value of electrochemical internal variables in a lithium-ion cell in real time, using readily available measurements of cell voltage, current, and temperature. The variables that can be estimated include any desired set of reaction flux and solid and electrolyte potentials and concentrations at any set of one-dimensional spatial locations, in addition to more standard quantities such as state of charge. The method uses an extended Kalman filter along with a one-dimensional physics-based reduced-order model of cell dynamics. Simulations show excellent and robust predictions having dependable error bounds for most internal variables. (C) 2014 Elsevier B.V. All rights reserved.

LHCb Kalman filter cross architecture studies

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00388548

2017-01-01

The 2020 upgrade of the LHCb detector will vastly increase the rate of collisions the Online system needs to process in software, in order to filter events in real time. 30 million collisions per second will pass through a selection chain, where each step is executed conditional to its prior acceptance. The Kalman filter is a fit applied to all reconstructed tracks which, due to its time characteristics and early execution in the selection chain, consumes 40% of the whole reconstruction time in the current trigger software. This makes the Kalman filter a time-critical component as the LHCb trigger evolves into a full software trigger in the Upgrade. I present a new Kalman filter algorithm for LHCb that can efficiently make use of any kind of SIMD processor, and its design is explained in depth. Performance benchmarks are compared between a variety of hardware architectures, including x86_64 and Power8, and the Intel Xeon Phi accelerator, and the suitability of said architectures to efficiently perform th...

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.

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.

Low-rank Kalman filtering for efficient state estimation of subsurface advective contaminant transport models

KAUST Repository

El Gharamti, Mohamad

2012-04-01

Accurate knowledge of the movement of contaminants in porous media is essential to track their trajectory and later extract them from the aquifer. A two-dimensional flow model is implemented and then applied on a linear contaminant transport model in the same porous medium. Because of different sources of uncertainties, this coupled model might not be able to accurately track the contaminant state. Incorporating observations through the process of data assimilation can guide the model toward the true trajectory of the system. The Kalman filter (KF), or its nonlinear invariants, can be used to tackle this problem. To overcome the prohibitive computational cost of the KF, the singular evolutive Kalman filter (SEKF) and the singular fixed Kalman filter (SFKF) are used, which are variants of the KF operating with low-rank covariance matrices. Experimental results suggest that under perfect and imperfect model setups, the low-rank filters can provide estimates as accurate as the full KF but at much lower computational effort. Low-rank filters are demonstrated to significantly reduce the computational effort of the KF to almost 3%. © 2012 American Society of Civil Engineers.

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.

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.

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.

Kalman and particle filtering methods for full vehicle and tyre identification

Science.gov (United States)

Bogdanski, Karol; Best, Matthew C.

2018-05-01

This paper considers identification of all significant vehicle handling dynamics of a test vehicle, including identification of a combined-slip tyre model, using only those sensors currently available on most vehicle controller area network buses. Using an appropriately simple but efficient model structure, all of the independent parameters are found from test vehicle data, with the resulting model accuracy demonstrated on independent validation data. The paper extends previous work on augmented Kalman Filter state estimators to concentrate wholly on parameter identification. It also serves as a review of three alternative filtering methods; identifying forms of the unscented Kalman filter, extended Kalman filter and particle filter are proposed and compared for effectiveness, complexity and computational efficiency. All three filters are suited to applications of system identification and the Kalman Filters can also operate in real-time in on-line model predictive controllers or estimators.

A Brief Tutorial on the Ensemble Kalman Filter

OpenAIRE

Mandel, Jan

2009-01-01

The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the sample covariance), and it is now an important data assimilation component of ensemble forecasting. EnKF is related to the particle filter (in this context, a particle is the s...

Fast Kalman-like filtering for large-dimensional linear and Gaussian state-space models

KAUST Repository

Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim

2015-01-01

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.

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.

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,

Adaptive Iterated Extended Kalman Filter and Its Application to Autonomous Integrated Navigation for Indoor Robot

Directory of Open Access Journals (Sweden)

Yuan Xu

2014-01-01

Full Text Available As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF which used the noise statistics estimator in the iterated extended Kalman (IEKF, and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS/wireless sensors networks (WSNs-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF.

Location Estimation for an Autonomously Guided Vehicle using an Augmented Kalman Filter to Autocalibrate the Odometry

DEFF Research Database (Denmark)

Larsen, Thomas Dall; Bak, Martin; Andersen, Nils Axel

1998-01-01

A Kalman filter using encoder readings as inputs and vision measurements as observations is designed as a location estimator for an autonomously guided vehicle (AGV). To reduce the effect of modelling errors an augmented filter that estimates the true system parameters is designed. The traditional...

Feedback Robust Cubature Kalman Filter for Target Tracking Using an Angle Sensor.

Science.gov (United States)

Wu, Hao; Chen, Shuxin; Yang, Binfeng; Chen, Kun

2016-05-09

The direction of arrival (DOA) tracking problem based on an angle sensor is an important topic in many fields. In this paper, a nonlinear filter named the feedback M-estimation based robust cubature Kalman filter (FMR-CKF) is proposed to deal with measurement outliers from the angle sensor. The filter designs a new equivalent weight function with the Mahalanobis distance to combine the cubature Kalman filter (CKF) with the M-estimation method. Moreover, by embedding a feedback strategy which consists of a splitting and merging procedure, the proper sub-filter (the standard CKF or the robust CKF) can be chosen in each time index. Hence, the probability of the outliers' misjudgment can be reduced. Numerical experiments show that the FMR-CKF performs better than the CKF and conventional robust filters in terms of accuracy and robustness with good computational efficiency. Additionally, the filter can be extended to the nonlinear applications using other types of sensors.

Impact of spectral smoothing on gamma radiation portal alarm probabilities

International Nuclear Information System (INIS)

Burr, T.; Hamada, M.; Hengartner, N.

2011-01-01

Gamma detector counts are included in radiation portal monitors (RPM) to screen for illicit nuclear material. Gamma counts are sometimes smoothed to reduce variance in the estimated underlying true mean count rate, which is the 'signal' in our context. Smoothing reduces total error variance in the estimated signal if the bias that smoothing introduces is more than offset by the variance reduction. An empirical RPM study for vehicle screening applications is presented for unsmoothed and smoothed gamma counts in low-resolution plastic scintillator detectors and in medium-resolution NaI detectors. - Highlights: → We evaluate options for smoothing counts from gamma detectors deployed for portal monitoring. → A new multiplicative bias correction (MBC) is shown to reduce bias in peak and valley regions. → Performance is measured using mean squared error and detection probabilities for sources. → Smoothing with the MBC improves detection probabilities and the mean squared error.

Prior knowledge processing for initial state of Kalman filter

Czech Academy of Sciences Publication Activity Database

Suzdaleva, Evgenia

2010-01-01

Roč. 24, č. 3 (2010), s. 188-202 ISSN 0890-6327 R&D Projects: GA ČR(CZ) GP201/06/P434 Institutional research plan: CEZ:AV0Z10750506 Keywords : Kalman filtering * prior knowledge * state-space model * initial state distribution Subject RIV: BC - Control Systems Theory Impact factor: 0.729, year: 2010 http://library.utia.cas.cz/separaty/2009/AS/suzdaleva-prior knowledge processing for initial state of kalman filter.pdf

Nonlinear Kalman Filtering in Affine Term Structure Models

DEFF Research Database (Denmark)

Christoffersen, Peter; Dorion, Christian; Jacobs, Kris

When the relationship between security prices and state variables in dynamic term structure models is nonlinear, existing studies usually linearize this relationship because nonlinear fi…ltering is computationally demanding. We conduct an extensive investigation of this linearization and analyze...... the potential of the unscented Kalman …filter to properly capture nonlinearities. To illustrate the advantages of the unscented Kalman …filter, we analyze the cross section of swap rates, which are relatively simple non-linear instruments, and cap prices, which are highly nonlinear in the states. An extensive...

On a New Family of Kalman Filter Algorithms for Integrated Navigation

Science.gov (United States)

Mahboub, V.; Saadatseresht, M.; Ardalan, A. A.

2017-09-01

Here we present a review on a new family of Kalman filter algorithms which recently developed for integrated navigation. In particular it is useful for vision based navigation due to the type of data. Here we mainly focus on three algorithms namely weighted Total Kalman filter (WTKF), integrated Kalman filter (IKF) and constrained integrated Kalman filter (CIKF). The common characteristic of these algorithms is that they can consider the neglected random observed quantities which may appear in the dynamic model. Moreover, our approach makes use of condition equations and straightforward variance propagation rules. The WTKF algorithm can deal with problems with arbitrary weight matrixes. Both of the observation equations and system equations can be dynamic-errors-in-variables (DEIV) models in the IKF algorithms. In some problems a quadratic constraint may exist. They can be solved by CIKF algorithm. Finally, we compare four algorithms WTKF, IKF, CIKF and EKF in numerical examples.

Instantaneous spectrum estimation of earthquake ground motions based on unscented Kalman filter method

Institute of Scientific and Technical Information of China (English)

无

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.

Machine learning of radial basis function neural network based on Kalman filter: Introduction

Directory of Open Access Journals (Sweden)

Vuković Najdan L.

2014-01-01

Full Text Available This paper analyzes machine learning of radial basis function neural network based on Kalman filtering. Three algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. We emphasize basic properties of these estimation algorithms, demonstrate how their advantages can be used for optimization of network parameters, derive mathematical models and show how they can be applied to model problems in engineering practice.

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's angular velocity is estimated by an Extended Kalman Filter which processes measurements of the rotor's angle. Sensorless control of the induction motor is again implemented through feedback of the estimated state vector. Additionally, a state estimation-based control loop is implemented using the Unscented Kalman Filter. Moreover, state estimation-based control is developed for the induction motor model using a nonlinear flatness-based controller and the state estimation that is provided by the Extended Kalman Filter. Unlike field oriented control, in the latter approach there is no assumption about decoupling between the rotor speed dynamics and the magnetic flux dynamics. The efficiency of the Kalman Filter-based control schemes, for both the DC and induction motor models, is evaluated through simulation experiments.

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.

Adaptive Kalman Filter of Transfer Alignment with Un-modeled Wing Flexure of Aircraft

Institute of Scientific and Technical Information of China (English)

无

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.

Particle rejuvenation of Rao-Blackwellized sequential Monte Carlo smoothers for conditionally linear and Gaussian models

Science.gov (United States)

Nguyen, Ngoc Minh; Corff, Sylvain Le; Moulines, Éric

2017-12-01

This paper focuses on sequential Monte Carlo approximations of smoothing distributions in conditionally linear and Gaussian state spaces. To reduce Monte Carlo variance of smoothers, it is typical in these models to use Rao-Blackwellization: particle approximation is used to sample sequences of hidden regimes while the Gaussian states are explicitly integrated conditional on the sequence of regimes and observations, using variants of the Kalman filter/smoother. The first successful attempt to use Rao-Blackwellization for smoothing extends the Bryson-Frazier smoother for Gaussian linear state space models using the generalized two-filter formula together with Kalman filters/smoothers. More recently, a forward-backward decomposition of smoothing distributions mimicking the Rauch-Tung-Striebel smoother for the regimes combined with backward Kalman updates has been introduced. This paper investigates the benefit of introducing additional rejuvenation steps in all these algorithms to sample at each time instant new regimes conditional on the forward and backward particles. This defines particle-based approximations of the smoothing distributions whose support is not restricted to the set of particles sampled in the forward or backward filter. These procedures are applied to commodity markets which are described using a two-factor model based on the spot price and a convenience yield for crude oil data.

On the evaluation of uncertainties for state estimation with the Kalman filter

International Nuclear Information System (INIS)

Eichstädt, S; Makarava, N; Elster, C

2016-01-01

The Kalman filter is an established tool for the analysis of dynamic systems with normally distributed noise, and it has been successfully applied in numerous areas. It provides sequentially calculated estimates of the system states along with a corresponding covariance matrix. For nonlinear systems, the extended Kalman filter is often used. This is derived from the Kalman filter by linearization around the current estimate. A key issue in metrology is the evaluation of the uncertainty associated with the Kalman filter state estimates. The ‘Guide to the Expression of Uncertainty in Measurement’ (GUM) and its supplements serve as the de facto standard for uncertainty evaluation in metrology. We explore the relationship between the covariance matrix produced by the Kalman filter and a GUM-compliant uncertainty analysis. In addition, the results of a Bayesian analysis are considered. For the case of linear systems with known system matrices, we show that all three approaches are compatible. When the system matrices are not precisely known, however, or when the system is nonlinear, this equivalence breaks down and different results can then be reached. For precisely known nonlinear systems, though, the result of the extended Kalman filter still corresponds to the linearized uncertainty propagation of the GUM. The extended Kalman filter can suffer from linearization and convergence errors. These disadvantages can be avoided to some extent by applying Monte Carlo procedures, and we propose such a method which is GUM-compliant and can also be applied online during the estimation. We illustrate all procedures in terms of a 2D dynamic system and compare the results with those obtained by particle filtering, which has been proposed for the approximate calculation of a Bayesian solution. Finally, we give some recommendations based on our findings. (paper)

Effectiveness of Variable-Gain Kalman Filter Based on Angle Error Calculated from Acceleration Signals in Lower Limb Angle Measurement with Inertial Sensors

Science.gov (United States)

Watanabe, Takashi

2013-01-01

The wearable sensor system developed by our group, which measured lower limb angles using Kalman-filtering-based method, was suggested to be useful in evaluation of gait function for rehabilitation support. However, it was expected to reduce variations of measurement errors. In this paper, a variable-Kalman-gain method based on angle error that was calculated from acceleration signals was proposed to improve measurement accuracy. The proposed method was tested comparing to fixed-gain Kalman filter and a variable-Kalman-gain method that was based on acceleration magnitude used in previous studies. First, in angle measurement in treadmill walking, the proposed method measured lower limb angles with the highest measurement accuracy and improved significantly foot inclination angle measurement, while it improved slightly shank and thigh inclination angles. The variable-gain method based on acceleration magnitude was not effective for our Kalman filter system. Then, in angle measurement of a rigid body model, it was shown that the proposed method had measurement accuracy similar to or higher than results seen in other studies that used markers of camera-based motion measurement system fixing on a rigid plate together with a sensor or on the sensor directly. The proposed method was found to be effective in angle measurement with inertial sensors. PMID:24282442

Three-phase electric drive with modified electronic smoothing inductor

DEFF Research Database (Denmark)

Singh, Yash Veer; Rasmussen, Peter Omand; Andersen, Torben Ole

2010-01-01

This paper presents a three-phase electric drive with a modified electronic smoothing inductor (MESI) having reduced size of passive components. The classical electronic smoothing inductor (ESI) is able to control a diode bridge output current and also reduce not only mains current harmonics...

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.

Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study.

Science.gov (United States)

Hosseinyalamdary, Siavash

2018-04-24

Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU), have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy.

Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter

Science.gov (United States)

Huang, Lei

2015-01-01

To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required. PMID:26437409

Autonomous underwater vehicle motion tracking using a Kalman filter for sensor fusion

CSIR Research Space (South Africa)

Holtzhausen, S

2008-11-01

Full Text Available it will be shown how a Kalman Filter is used to estimate the position of an autonomous vehicle in a three dimensional space. The Kalman filter is used to estimate movement and position using measurements from multiple sensors...

Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles

International Nuclear Information System (INIS)

Sun, Fengchun; Hu, Xiaosong; Zou, Yuan; Li, Siguang

2011-01-01

An accurate battery State of Charge estimation is of great significance for battery electric vehicles and hybrid electric vehicles. This paper presents an adaptive unscented Kalman filtering method to estimate State of Charge of a lithium-ion battery for battery electric vehicles. The adaptive adjustment of the noise covariances in the State of Charge estimation process is implemented by an idea of covariance matching in the unscented Kalman filter context. Experimental results indicate that the adaptive unscented Kalman filter-based algorithm has a good performance in estimating the battery State of Charge. A comparison with the adaptive extended Kalman filter, extended Kalman filter, and unscented Kalman filter-based algorithms shows that the proposed State of Charge estimation method has a better accuracy. -- Highlights: → Adaptive unscented Kalman filtering is proposed to estimate State of Charge of a lithium-ion battery for electric vehicles. → The proposed method has a good performance in estimating the battery State of Charge. → A comparison with three other Kalman filtering algorithms shows that the proposed method has a better accuracy.

A Kalman Filtering Perspective for Multiatlas Segmentation*

Science.gov (United States)

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

2016-01-01

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. PMID:26807162

Practical feasibility of Kalman filters for the state estimation of lithium-ion batteries

OpenAIRE

Campestrini, Christian

2018-01-01

This work investigates the feasibility of the Kalman filter for the state estimation of lithium-ion cells and modules under real conditions. Therefore, the dependencies of the cells during ageing are shown and various Kalman filter types are compared. The strongly varying model parameters, as well as the temperature and ageing dependent open circuit voltage, require an empirical adaptation of the inconstant and non-linear filter tuning parameters. The performance of the Kalman filter in a rea...

Forecasting with the Standardized Self-Perturbed Kalman Filter

DEFF Research Database (Denmark)

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

We propose and study the finite-sample properties of a modified version of the self-perturbed Kalman filter of Park and Jun (1992) for the on-line estimation of models subject to parameter instability. The perturbation term in the updating equation of the state covariance matrix is now weighted...... 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....

Teknik Rektifikasi Citra dan Tapis Kalman Dalam Mengestimasi Kecepatan Kendaraan

Directory of Open Access Journals (Sweden)

Rika Favoria Gusa

2014-03-01

Full Text Available Estimating is a challenging task when the image sequence from a camera are directly processed because there is perspective projection that causes length and area ratio of objects in the image are not preserved. In this paper, it was used image rectification technique and Kalman filter algorithm to overcome the problems encountered in order to obtain accurate vehicle velocity estimation. Rectified images as result of image rectification were processed, then Kalman filter algorithm was executed based on the processing result of the rectified images. The result of the tests showed that geometric distortion on the objects in the image sequence could be corrected well by using image rectification. Kalman filter algorithm was also good enough in estimating vehicle velocity. The error of average velocity estimation was ±3 km/hour.

Neural network training by Kalman filtering in process system monitoring

International Nuclear Information System (INIS)

Ciftcioglu, Oe.

1996-03-01

Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)

Design considerations for a suboptimal Kalman filter

Science.gov (United States)

Difilippo, D. J.

1995-06-01

In designing a suboptimal Kalman filter, the designer must decide how to simplify the system error model without causing the filter estimation errors to increase to unacceptable levels. Deletion of certain error states and decoupling of error state dynamics are the two principal model simplifications that are commonly used in suboptimal filter design. For the most part, the decisions as to which error states can be deleted or decoupled are based on the designer's understanding of the physics of the particular system. Consequently, the details of a suboptimal design are usually unique to the specific application. In this paper, the process of designing a suboptimal Kalman filter is illustrated for the case of an airborne transfer-of-alignment (TOA) system used for synthetic aperture radar (SAR) motion compensation. In this application, the filter must continuously transfer the alignment of an onboard Doppler-damped master inertial navigation system (INS) to a strapdown navigator that processes information from a less accurate inertial measurement unit (IMU) mounted on the radar antenna. The IMU is used to measure spurious antenna motion during the SAR imaging interval, so that compensating phase corrections can be computed and applied to the radar returns, thereby presenting image degradation that would otherwise result from such motions. The principles of SAR are described in many references, for instance. The primary function of the TOA Kalman filter in a SAR motion compensation system is to control strapdown navigator attitude errors, and to a less degree, velocity and heading errors. Unlike a classical navigation application, absolute positional accuracy is not important. The motion compensation requirements for SAR imaging are discussed in some detail. This TOA application is particularly appropriate as a vehicle for discussing suboptimal filter design, because the system contains features that can be exploited to allow both deletion and decoupling of error

Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study

Directory of Open Access Journals (Sweden)

Siavash Hosseinyalamdary

2018-04-01

Full Text Available Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU, have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy.

Ensemble Kalman filtering with residual nudging

KAUST Repository

Luo, X.; Hoteit, Ibrahim

2012-01-01

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

Spectral Diagonal Ensemble Kalman Filters

Czech Academy of Sciences Publication Activity Database

Kasanický, Ivan; Mandel, Jan; Vejmelka, Martin

2015-01-01

Roč. 22, č. 4 (2015), s. 485-497 ISSN 1023-5809 R&D Projects: GA ČR GA13-34856S Grant - others:NSF(US) DMS-1216481 Institutional support: RVO:67985807 Keywords : data assimilation * ensemble Kalman filter * spectral representation Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.321, year: 2015

The discrete Kalman filtering approach for seismic signals deconvolution

International Nuclear Information System (INIS)

Kurniadi, Rizal; Nurhandoko, Bagus Endar B.

2012-01-01

Seismic signals are a convolution of reflectivity and seismic wavelet. One of the most important stages in seismic data processing is deconvolution process; the process of deconvolution is inverse filters based on Wiener filter theory. This theory is limited by certain modelling assumptions, which may not always valid. The discrete form of the Kalman filter is then used to generate an estimate of the reflectivity function. The main advantage of Kalman filtering is capability of technique to handling continually time varying models and has high resolution capabilities. In this work, we use discrete Kalman filter that it was combined with primitive deconvolution. Filtering process works on reflectivity function, hence the work flow of filtering is started with primitive deconvolution using inverse of wavelet. The seismic signals then are obtained by convoluting of filtered reflectivity function with energy waveform which is referred to as the seismic wavelet. The higher frequency of wavelet gives smaller wave length, the graphs of these results are presented.

Kalman filter analysis of delayed neutron nondestructive assay measurements

International Nuclear Information System (INIS)

Aumeier, S. E.

1998-01-01

The ability to nondestructively determine the presence and quantity of fissile and fertile nuclei in various matrices is important in several nuclear applications including international and domestics safeguards, radioactive waste characterization and nuclear facility operations. Material irradiation followed by delayed neutron counting is a well known and useful nondestructive assay technique used to determine the fissile-effective content of assay samples. Previous studies have demonstrated the feasibility of using Kalman filters to unfold individual isotopic contributions to delayed neutron measurements resulting from the assay of mixes of uranium and plutonium isotopes. However, the studies in question used simulated measurement data and idealized parameters. We present the results of the Kalman filter analysis of several measurements of U/Pu mixes taken using Argonne National Laboratory's delayed neutron nondestructive assay device. The results demonstrate the use of Kalman filters as a signal processing tool to determine the fissile and fertile isotopic content of an assay sample from the aggregate delayed neutron response following neutron irradiation

Event-triggered Kalman-consensus filter for two-target tracking sensor networks.

Science.gov (United States)

Su, Housheng; Li, Zhenghao; Ye, Yanyan

2017-11-01

This paper is concerned with the problem of event-triggered Kalman-consensus filter for two-target tracking sensor networks. According to the event-triggered protocol and the mean-square analysis, a suboptimal Kalman gain matrix is derived and a suboptimal event-triggered distributed filter is obtained. Based on the Kalman-consensus filter protocol, all sensors which only depend on its neighbors' information can track their corresponding targets. Furthermore, utilizing Lyapunov method and matrix theory, some sufficient conditions are presented for ensuring the stability of the system. Finally, a simulation example is presented to verify the effectiveness of the proposed event-triggered protocol. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Application of Kalman Filter for Estimating a Process Disturbance in a Building Space

Directory of Open Access Journals (Sweden)

Deuk-Woo Kim

2017-10-01

Full Text Available This paper addresses an application of the Kalman filter for estimating a time-varying process disturbance in a building space. The process disturbance means a synthetic composite of heat gains and losses caused by internal heat sources e.g., people, lights, equipment, and airflows. It is difficult to measure and quantify the internal heat sources and airflows due to their dynamic nature and time-lag impact on indoor environment. To address this issue, a Kalman filter estimation method was used in this study. The Kalman filtering is well suited for situations when state variables of interest cannot be measured. Based on virtual and real experiments conducted in this study, it was found that the Kalman filter can be used to estimate the time-varying process disturbance in a building space.

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

Institute of Scientific and Technical Information of China (English)

无

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.

State and parameter estimation in a nuclear fuel pin using the extended Kalman filter

International Nuclear Information System (INIS)

Feeley, J.J.

1979-03-01

The Kalman filter is a powerful tool for the design and analysis of stochastic systems. The general nature of the method permits such diverse applications as on-line state estimation in optimal control systems, as well as state and parameter estimation applications in data analysis and system identification. However, while there have been a large number of Kalman filter applications in the aerospace industry, there have been relatively few in the nuclear industry. The report describes some initial efforts made at the Idaho National Engineering Laboratory to gain experience with the methods of Kalman filtering and to test their applicability to nuclear engineering problems. Two specific cases were considered: first, a real-time state estimation problem using a hybrid computer where the process was simulated on the analog portion of the computer, and the Kalman filter was programmed on the digital portion; second, a system identification problem where a digital extended Kalman filter program was used to estimate states and parameters in a nuclear fuel pin using data generated both by actual experiments and computer simulations. The report contains a derivation of the Kalman filter equations, a development of the mathematical model of the nuclear fuel pin, a description of the computer programs used in the analysis, and a discussion of the results obtained

Mapping global precipitation with satellite borne microwave radiometer and infrared radiometer using Kalman filter

International Nuclear Information System (INIS)

Noda, S.; Sasashige, K.; Katagami, D.; Ushio, T.; Kubota, T.; Okamoto, K.; Iida, Y.; Kida, S.; Shige, S.; Shimomura, S.; Aonashi, K.; Inoue, T.; Morimoto, T.; Kawasaki, Z.

2007-01-01

Estimates of precipitation at a high time and space resolution are required for many important applications. In this paper, a new global precipitation map with high spatial (0.1 degree) and temporal (1 hour) resolution using Kalman filter technique is presented and evaluated. Infrared radiometer data, which are available globally nearly everywhere and nearly all the time from geostationary orbit, are used with the several microwave radiometers aboard the LEO satellites. IR data is used as a means to move the precipitation estimates from microwave observation during periods when microwave data are not available at a given location. Moving vector is produced by computing correlations on successive images of IR data. When precipitation is moved, the Kalman filter is applied for improving the moving technique in this research. The new approach showed a better score than the technique without Kalman filter. The correlation coefficient was 0.1 better than without the Kalman filter about 6 hours after the last microwave overpasses, and the RMS error was improved about 0.1 mm/h with the Kalman filter technique. This approach is unique in that 1) the precipitation estimates from the microwave radiometer is mainly used, 2) the IR temperature in every hour is also used for the precipitation estimates based on the Kalman filter theory

Improving short-range ensemble Kalman storm surge forecasting using robust adaptive inflation

NARCIS (Netherlands)

Altaf, M.U.; Butler, T.; Luo, X.; Dawson, C.; Mayo, T.; Hoteit, I.

2013-01-01

This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H? filter. By design, an H? filter is more robust than the common Kalman filter in the sense

Rotation speed measurement for turbine governor: torsion filtering by using Kalman filter

International Nuclear Information System (INIS)

Houry, M.P.; Bourles, H.

1996-01-01

The rotation speed of a turbogenerator is disturbed by its shaft torsion. Obtaining a filtered measure of this speed is a problem of a great practical importance for turbine governor. A good filtering of this speed must meet two requirements: it must cut frequencies of the shaft torsion oscillation and it must not reduce or delay the signal in the pass-band, i.e. at lower frequencies. At Electricite de France, the speed measure is used to set in motion the fast valving system as quickly as possible, after a short circuit close to the unit or rather an islanding. It is difficult to satisfy these two requirements by using conventional filtering methods. The standard solution consists in a first order filter: at Electricite de France, its time constant is equal to 80 ms. We have decided to improve this filtering by designing a new filter which cuts the frequencies of the shaft torsion oscillation without reducing the bandwidth to the speed measure. If one uses conventional methods to obtain a band stop filter, it is easy to obtain the desired magnitude but not a phase near zero in the whole pass-band. Therefore, we have chosen to design the filter by using Kalman'a theory. The measurement noise is modeled as a colored one, generated by a very lightly damped system driven by a while noise. The resulting Kalman filter is an effective band stop filter, whose phase nicely remains near zero in the whole pass-band. The digital simulations we made and the tests we carried out with the Electricite de France Micro Network laboratory show the advantages of the rotation speed filter we designed using Kalman's theory. With the proposed filter, the speed measure filtering is better in terms of reduction and phase shift. the result is that there are less untimely solicitations of the fast valving system. Consequently, this device improves the power systems stability by minimizing the risks of deep perturbations due to a temporary lack of generation and the risks of under-speed loss

Application of wavelet-based multi-model Kalman filters to real-time flood forecasting

Science.gov (United States)

Chou, Chien-Ming; Wang, Ru-Yih

2004-04-01

This paper presents the application of a multimodel method using a wavelet-based Kalman filter (WKF) bank to simultaneously estimate decomposed state variables and unknown parameters for real-time flood forecasting. Applying the Haar wavelet transform alters the state vector and input vector of the state space. In this way, an overall detail plus approximation describes each new state vector and input vector, which allows the WKF to simultaneously estimate and decompose state variables. The wavelet-based multimodel Kalman filter (WMKF) is a multimodel Kalman filter (MKF), in which the Kalman filter has been substituted for a WKF. The WMKF then obtains M estimated state vectors. Next, the M state-estimates, each of which is weighted by its possibility that is also determined on-line, are combined to form an optimal estimate. Validations conducted for the Wu-Tu watershed, a small watershed in Taiwan, have demonstrated that the method is effective because of the decomposition of wavelet transform, the adaptation of the time-varying Kalman filter and the characteristics of the multimodel method. Validation results also reveal that the resulting method enhances the accuracy of the runoff prediction of the rainfall-runoff process in the Wu-Tu watershed.

Extended Kalman filtering applied to a two-axis robotic arm with flexible links

Energy Technology Data Exchange (ETDEWEB)

Lertpiriyasuwat, V.; Berg, M.C.; Buffinton, K.W.

2000-03-01

An industrial robot today uses measurements of its joint positions and models of its kinematics and dynamics to estimate and control its end-effector position. Substantially better end-effector position estimation and control performance would be obtainable if direct measurements of its end-effector position were also used. The subject of this paper is extended Kalman filtering for precise estimation of the position of the end-effector of a robot using, in addition to the usual measurements of the joint positions, direct measurements of the end-effector position. The estimation performances of extended Kalman filters are compared in applications to a planar two-axis robotic arm with very flexible links. The comparisons shed new light on the dependence of extended Kalman filter estimation performance on the quality of the model of the arm dynamics that the extended Kalman filter operates with.

Direct and accelerated parameter mapping using the unscented Kalman filter.

Science.gov (United States)

Zhao, Li; Feng, Xue; Meyer, Craig H

2016-05-01

To accelerate parameter mapping using a new paradigm that combines image reconstruction and model regression as a parameter state-tracking problem. In T2 mapping, the T2 map is first encoded in parameter space by multi-TE measurements and then encoded by Fourier transformation with readout/phase encoding gradients. Using a state transition function and a measurement function, the unscented Kalman filter can describe T2 mapping as a dynamic system and directly estimate the T2 map from the k-space data. The proposed method was validated with a numerical brain phantom and volunteer experiments with a multiple-contrast spin echo sequence. Its performance was compared with a conjugate-gradient nonlinear inversion method at undersampling factors of 2 to 8. An accelerated pulse sequence was developed based on this method to achieve prospective undersampling. Compared with the nonlinear inversion reconstruction, the proposed method had higher precision, improved structural similarity and reduced normalized root mean squared error, with acceleration factors up to 8 in numerical phantom and volunteer studies. This work describes a new perspective on parameter mapping by state tracking. The unscented Kalman filter provides a highly accelerated and efficient paradigm for T2 mapping. © 2015 Wiley Periodicals, Inc.

An adaptive three-stage extended Kalman filter for nonlinear discrete-time system in presence of unknown inputs.

Science.gov (United States)

Xiao, Mengli; Zhang, Yongbo; Wang, Zhihua; Fu, Huimin

2018-04-01

Considering the performances of conventional Kalman filter may seriously degrade when it suffers stochastic faults and unknown input, which is very common in engineering problems, a new type of adaptive three-stage extended Kalman filter (AThSEKF) is proposed to solve state and fault estimation in nonlinear discrete-time system under these conditions. The three-stage UV transformation and adaptive forgetting factor are introduced for derivation, and by comparing with the adaptive augmented state extended Kalman filter, it is proven to be uniformly asymptotically stable. Furthermore, the adaptive three-stage extended Kalman filter is applied to a two-dimensional radar tracking scenario to illustrate the effect, and the performance is compared with that of conventional three stage extended Kalman filter (ThSEKF) and the adaptive two-stage extended Kalman filter (ATEKF). The results show that the adaptive three-stage extended Kalman filter is more effective than these two filters when facing the nonlinear discrete-time systems with information of unknown inputs not perfectly known. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Maximum Correntropy Unscented Kalman Filter for Ballistic Missile Navigation System based on SINS/CNS Deeply Integrated Mode.

Science.gov (United States)

Hou, Bowen; He, Zhangming; Li, Dong; Zhou, Haiyin; Wang, Jiongqi

2018-05-27

Strap-down inertial navigation system/celestial navigation system ( SINS/CNS) integrated navigation is a high precision navigation technique for ballistic missiles. The traditional navigation method has a divergence in the position error. A deeply integrated mode for SINS/CNS navigation system is proposed to improve the navigation accuracy of ballistic missile. The deeply integrated navigation principle is described and the observability of the navigation system is analyzed. The nonlinearity, as well as the large outliers and the Gaussian mixture noises, often exists during the actual navigation process, leading to the divergence phenomenon of the navigation filter. The new nonlinear Kalman filter on the basis of the maximum correntropy theory and unscented transformation, named the maximum correntropy unscented Kalman filter, is deduced, and the computational complexity is analyzed. The unscented transformation is used for restricting the nonlinearity of the system equation, and the maximum correntropy theory is used to deal with the non-Gaussian noises. Finally, numerical simulation illustrates the superiority of the proposed filter compared with the traditional unscented Kalman filter. The comparison results show that the large outliers and the influence of non-Gaussian noises for SINS/CNS deeply integrated navigation is significantly reduced through the proposed filter.

Extended Kalman Filter Modifications Based on an Optimization View Point

OpenAIRE

Skoglund, Martin; Hendeby, Gustaf; Axehill, Daniel

2015-01-01

The extended Kalman filter (EKF) has been animportant tool for state estimation of nonlinear systems sinceits introduction. However, the EKF does not possess the same optimality properties as the Kalman filter, and may perform poorly. By viewing the EKF as an optimization problem it is possible to, in many cases, improve its performance and robustness. The paper derives three variations of the EKF by applying different optimisation algorithms to the EKF costfunction and relate these to the it...

Deterministic Mean-Field Ensemble Kalman Filtering

KAUST Repository

Law, Kody

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.

Deterministic Mean-Field Ensemble Kalman Filtering

KAUST Repository

Law, Kody; Tembine, Hamidou; Tempone, Raul

2016-01-01

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.

Polarization beam smoothing for inertial confinement fusion

International Nuclear Information System (INIS)

Rothenberg, Joshua E.

2000-01-01

For both direct and indirect drive approaches to inertial confinement fusion (ICF) it is imperative to obtain the best possible drive beam uniformity. The approach chosen for the National Ignition Facility uses a random-phase plate to generate a speckle pattern with a precisely controlled envelope on target. A number of temporal smoothing techniques can then be employed to utilize bandwidth to rapidly change the speckle pattern, and thus average out the small-scale speckle structure. One technique which generally can supplement other smoothing methods is polarization smoothing (PS): the illumination of the target with two distinct and orthogonally polarized speckle patterns. Since these two polarizations do not interfere, the intensity patterns add incoherently, and the rms nonuniformity can be reduced by a factor of (√2). A number of PS schemes are described and compared on the basis of the aggregate rms and the spatial spectrum of the focused illumination distribution. The (√2) rms nonuniformity reduction of PS is present on an instantaneous basis and is, therefore, of particular interest for the suppression of laser plasma instabilities, which have a very rapid response time. When combining PS and temporal methods, such as smoothing by spectral dispersion (SSD), PS can reduce the rms of the temporally smoothed illumination by an additional factor of (√2). However, it has generally been thought that in order to achieve this reduction of (√2), the increased divergence of the beam from PS must exceed the divergence of SSD. It is also shown here that, over the time scales of interest to direct or indirect drive ICF, under some conditions PS can reduce the smoothed illumination rms by nearly (√2) even when the PS divergence is much smaller than that of SSD. (c) 2000 American Institute of Physics

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.

Fire spread estimation on forest wildfire using ensemble kalman filter

Science.gov (United States)

Syarifah, Wardatus; Apriliani, Erna

2018-04-01

Wildfire is one of the most frequent disasters in the world, for example forest wildfire, causing population of forest decrease. Forest wildfire, whether naturally occurring or prescribed, are potential risks for ecosystems and human settlements. These risks can be managed by monitoring the weather, prescribing fires to limit available fuel, and creating firebreaks. With computer simulations we can predict and explore how fires may spread. The model of fire spread on forest wildfire was established to determine the fire properties. The fire spread model is prepared based on the equation of the diffusion reaction model. There are many methods to estimate the spread of fire. The Kalman Filter Ensemble Method is a modified estimation method of the Kalman Filter algorithm that can be used to estimate linear and non-linear system models. In this research will apply Ensemble Kalman Filter (EnKF) method to estimate the spread of fire on forest wildfire. Before applying the EnKF method, the fire spread model will be discreted using finite difference method. At the end, the analysis obtained illustrated by numerical simulation using software. The simulation results show that the Ensemble Kalman Filter method is closer to the system model when the ensemble value is greater, while the covariance value of the system model and the smaller the measurement.

Sampling strong tracking nonlinear unscented Kalman filter and its application in eye tracking

International Nuclear Information System (INIS)

Zu-Tao, Zhang; Jia-Shu, Zhang

2010-01-01

The unscented Kalman filter is a developed well-known method for nonlinear motion estimation and tracking. However, the standard unscented Kalman filter has the inherent drawbacks, such as numerical instability and much more time spent on calculation in practical applications. In this paper, we present a novel sampling strong tracking nonlinear unscented Kalman filter, aiming to overcome the difficulty in nonlinear eye tracking. In the above proposed filter, the simplified unscented transform sampling strategy with n + 2 sigma points leads to the computational efficiency, and suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking. Compared with the related unscented Kalman filter for eye tracking, the proposed filter has potential advantages in robustness, convergence speed, and tracking accuracy. The final experimental results show the validity of our method for eye tracking under realistic conditions. (classical areas of phenomenology)

Frequency-scanning interferometry using a time-varying Kalman filter for dynamic tracking measurements.

Science.gov (United States)

Jia, Xingyu; Liu, Zhigang; Tao, Long; Deng, Zhongwen

2017-10-16

Frequency scanning interferometry (FSI) with a single external cavity diode laser (ECDL) and time-invariant Kalman filtering is an effective technique for measuring the distance of a dynamic target. However, due to the hysteresis of the piezoelectric ceramic transducer (PZT) actuator in the ECDL, the optical frequency sweeps of the ECDL exhibit different behaviors, depending on whether the frequency is increasing or decreasing. Consequently, the model parameters of Kalman filter appear time varying in each iteration, which produces state estimation errors with time-invariant filtering. To address this, in this paper, a time-varying Kalman filter is proposed to model the instantaneous movement of a target relative to the different optical frequency tuning durations of the ECDL. The combination of the FSI method with the time-varying Kalman filter was theoretically analyzed, and the simulation and experimental results show the proposed method greatly improves the performance of dynamic FSI measurements.

Low-Rank Kalman Filtering in Subsurface Contaminant Transport Models

KAUST Repository

El Gharamti, Mohamad

2010-01-01

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.

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.

Fundamental aspects of the Kalman filter with examples regarding load forecasting and acid rain

Energy Technology Data Exchange (ETDEWEB)

Molenaar, J.; Visser, H.

1989-02-01

Time-series analysis has become an important tool in research fields such as econometrics, medicine, environmental sciences etc. The Kalman filter is a powerful algorithm for estimation of a wide variety of time-series models. A detailed derivation of the Kalman filter formulae is presented in this contribution. It is also shown how a class of time-series models, the so-called structural models, can be estimated by the Kalman filter. Two examples related to electricity generation are described. 5 figs., 22 refs.

Research on Kalman-filter based multisensor data fusion

Institute of Scientific and Technical Information of China (English)

无

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.

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.

A Fixed-Lag Kalman Smoother to Filter Power Line Interference in Electrocardiogram Recordings.

Science.gov (United States)

Warmerdam, G J J; Vullings, R; Schmitt, L; Van Laar, J O E H; Bergmans, J W M

2017-08-01

Filtering power line interference (PLI) from electrocardiogram (ECG) recordings can lead to significant distortions of the ECG and mask clinically relevant features in ECG waveform morphology. The objective of this study is to filter PLI from ECG recordings with minimal distortion of the ECG waveform. In this paper, we propose a fixed-lag Kalman smoother with adaptive noise estimation. The performance of this Kalman smoother in filtering PLI is compared to that of a fixed-bandwidth notch filter and several adaptive PLI filters that have been proposed in the literature. To evaluate the performance, we corrupted clean neonatal ECG recordings with various simulated PLI. Furthermore, examples are shown of filtering real PLI from an adult and a fetal ECG recording. The fixed-lag Kalman smoother outperforms other PLI filters in terms of step response settling time (improvements that range from 0.1 to 1 s) and signal-to-noise ratio (improvements that range from 17 to 23 dB). Our fixed-lag Kalman smoother can be used for semi real-time applications with a limited delay of 0.4 s. The fixed-lag Kalman smoother presented in this study outperforms other methods for filtering PLI and leads to minimal distortion of the ECG waveform.

Kalman filter with a linear state model for PDR+WLAN positioning and its application to assisting a particle filter

Science.gov (United States)

Raitoharju, Matti; Nurminen, Henri; Piché, Robert

2015-12-01

Indoor positioning based on wireless local area network (WLAN) signals is often enhanced using pedestrian dead reckoning (PDR) based on an inertial measurement unit. The state evolution model in PDR is usually nonlinear. We present a new linear state evolution model for PDR. In simulated-data and real-data tests of tightly coupled WLAN-PDR positioning, the positioning accuracy with this linear model is better than with the traditional models when the initial heading is not known, which is a common situation. The proposed method is computationally light and is also suitable for smoothing. Furthermore, we present modifications to WLAN positioning based on Gaussian coverage areas and show how a Kalman filter using the proposed model can be used for integrity monitoring and (re)initialization of a particle filter.

Fractional kalman filter to estimate the concentration of air pollution

Science.gov (United States)

Vita Oktaviana, Yessy; Apriliani, Erna; Khusnul Arif, Didik

2018-04-01

Air pollution problem gives important effect in quality environment and quality of human’s life. Air pollution can be caused by nature sources or human activities. Pollutant for example Ozone, a harmful gas formed by NOx and volatile organic compounds (VOCs) emitted from various sources. The air pollution problem can be modeled by TAPM-CTM (The Air Pollution Model with Chemical Transport Model). The model shows concentration of pollutant in the air. Therefore, it is important to estimate concentration of air pollutant. Estimation method can be used for forecast pollutant concentration in future and keep stability of air quality. In this research, an algorithm is developed, based on Fractional Kalman Filter to solve the model of air pollution’s problem. The model will be discretized first and then it will be estimated by the method. The result shows that estimation of Fractional Kalman Filter has better accuracy than estimation of Kalman Filter. The accuracy was tested by applying RMSE (Root Mean Square Error).

Kalman filter for statistical monitoring of forest cover across sub-continental regions

Science.gov (United States)

Raymond L. Czaplewski

1991-01-01

The Kalman filter is a multivariate generalization of the composite estimator which recursively combines a current direct estimate with a past estimate that is updated for expected change over time with a prediction model. The Kalman filter can estimate proportions of different cover types for sub-continental regions each year. A random sample of high-resolution...

An extended Kalman-Bucy filter for atmospheric temperature profile retrieval with a passive microwave sounder

Science.gov (United States)

Ledsham, W. H.; Staelin, D. H.

1978-01-01

An extended Kalman-Bucy filter has been implemented for atmospheric temperature profile retrievals from observations made using the Scanned Microwave Spectrometer (SCAMS) instrument carried on the Nimbus 6 satellite. This filter has the advantage that it requires neither stationary statistics in the underlying processes nor linear production of the observed variables from the variables to be estimated. This extended Kalman-Bucy filter has yielded significant performance improvement relative to multiple regression retrieval methods. A multi-spot extended Kalman-Bucy filter has also been developed in which the temperature profiles at a number of scan angles in a scanning instrument are retrieved simultaneously. These multi-spot retrievals are shown to outperform the single-spot Kalman retrievals.

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

OpenAIRE

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. Perf...

Hybrid Kalman and unscented Kalman filters for INS/GPS integrated system considering constant lever arm effect

Institute of Scientific and Technical Information of China (English)

常国宾; 柳明

2015-01-01

In inertial navigation system (INS) and global positioning system (GPS) integrated system, GPS antennas are usually not located at the same location as the inertial measurement unit (IMU) of the INS, so the lever arm effect exists, which makes the observation equation highly nonlinear. The INS/GPS integration with constant lever arm effect is studied. The position relation of IMU and GPS’s antenna is represented in the earth centered earth fixed frame, while the velocity relation of these two systems is represented in local horizontal frame. Due to the small integration time interval of INS, i.e. 0.1 s in this work, the nonlinearity in the INS error equation is trivial, so the linear INS error model is constructed and addressed by Kalman filter’s prediction step. On the other hand, the high nonlinearity in the observation equation due to lever arm effect is addressed by unscented Kalman filter’s update step to attain higher accuracy and better applicability. Simulation is designed and the performance of the hybrid filter is validated.

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

2017-11-01

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.

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. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

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.

Stock price estimation using ensemble Kalman Filter square root method

Science.gov (United States)

Karya, D. F.; Katias, P.; Herlambang, T.

2018-04-01

Shares are securities as the possession or equity evidence of an individual or corporation over an enterprise, especially public companies whose activity is stock trading. Investment in stocks trading is most likely to be the option of investors as stocks trading offers attractive profits. In determining a choice of safe investment in the stocks, the investors require a way of assessing the stock prices to buy so as to help optimize their profits. An effective method of analysis which will reduce the risk the investors may bear is by predicting or estimating the stock price. Estimation is carried out as a problem sometimes can be solved by using previous information or data related or relevant to the problem. The contribution of this paper is that the estimates of stock prices in high, low, and close categorycan be utilized as investors’ consideration for decision making in investment. In this paper, stock price estimation was made by using the Ensemble Kalman Filter Square Root method (EnKF-SR) and Ensemble Kalman Filter method (EnKF). The simulation results showed that the resulted estimation by applying EnKF method was more accurate than that by the EnKF-SR, with an estimation error of about 0.2 % by EnKF and an estimation error of 2.6 % by EnKF-SR.

An Improved Strong Tracking Cubature Kalman Filter for GPS/INS Integrated Navigation Systems.

Science.gov (United States)

Feng, Kaiqiang; Li, Jie; Zhang, Xi; Zhang, Xiaoming; Shen, Chong; Cao, Huiliang; Yang, Yanyu; Liu, Jun

2018-06-12

The cubature Kalman filter (CKF) is widely used in the application of GPS/INS integrated navigation systems. However, its performance may decline in accuracy and even diverge in the presence of process uncertainties. To solve the problem, a new algorithm named improved strong tracking seventh-degree spherical simplex-radial cubature Kalman filter (IST-7thSSRCKF) is proposed in this paper. In the proposed algorithm, the effect of process uncertainty is mitigated by using the improved strong tracking Kalman filter technique, in which the hypothesis testing method is adopted to identify the process uncertainty and the prior state estimate covariance in the CKF is further modified online according to the change in vehicle dynamics. In addition, a new seventh-degree spherical simplex-radial rule is employed to further improve the estimation accuracy of the strong tracking cubature Kalman filter. In this way, the proposed comprehensive algorithm integrates the advantage of 7thSSRCKF’s high accuracy and strong tracking filter’s strong robustness against process uncertainties. The GPS/INS integrated navigation problem with significant dynamic model errors is utilized to validate the performance of proposed IST-7thSSRCKF. Results demonstrate that the improved strong tracking cubature Kalman filter can achieve higher accuracy than the existing CKF and ST-CKF, and is more robust for the GPS/INS integrated navigation system.

Maximum Correntropy Unscented Kalman Filter for Ballistic Missile Navigation System based on SINS/CNS Deeply Integrated Mode

Directory of Open Access Journals (Sweden)

Bowen Hou

2018-05-01

Full Text Available Strap-down inertial navigation system/celestial navigation system ( SINS/CNS integrated navigation is a high precision navigation technique for ballistic missiles. The traditional navigation method has a divergence in the position error. A deeply integrated mode for SINS/CNS navigation system is proposed to improve the navigation accuracy of ballistic missile. The deeply integrated navigation principle is described and the observability of the navigation system is analyzed. The nonlinearity, as well as the large outliers and the Gaussian mixture noises, often exists during the actual navigation process, leading to the divergence phenomenon of the navigation filter. The new nonlinear Kalman filter on the basis of the maximum correntropy theory and unscented transformation, named the maximum correntropy unscented Kalman filter, is deduced, and the computational complexity is analyzed. The unscented transformation is used for restricting the nonlinearity of the system equation, and the maximum correntropy theory is used to deal with the non-Gaussian noises. Finally, numerical simulation illustrates the superiority of the proposed filter compared with the traditional unscented Kalman filter. The comparison results show that the large outliers and the influence of non-Gaussian noises for SINS/CNS deeply integrated navigation is significantly reduced through the proposed filter.

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.

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.

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.

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.

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.

Multilevel ensemble Kalman filtering

KAUST Repository

Hoel, Hakon; Law, Kody J. H.; Tempone, Raul

2016-01-01

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.

Assessment of reduced-order unscented Kalman filter for parameter identification in 1-dimensional blood flow models using experimental data.

Science.gov (United States)

Caiazzo, A; Caforio, Federica; Montecinos, Gino; Muller, Lucas O; Blanco, Pablo J; Toro, Eluterio F

2016-10-25

This work presents a detailed investigation of a parameter estimation approach on the basis of the reduced-order unscented Kalman filter (ROUKF) in the context of 1-dimensional blood flow models. In particular, the main aims of this study are (1) to investigate the effects of using real measurements versus synthetic data for the estimation procedure (i.e., numerical results of the same in silico model, perturbed with noise) and (2) to identify potential difficulties and limitations of the approach in clinically realistic applications to assess the applicability of the filter to such setups. For these purposes, the present numerical study is based on a recently published in vitro model of the arterial network, for which experimental flow and pressure measurements are available at few selected locations. To mimic clinically relevant situations, we focus on the estimation of terminal resistances and arterial wall parameters related to vessel mechanics (Young's modulus and wall thickness) using few experimental observations (at most a single pressure or flow measurement per vessel). In all cases, we first perform a theoretical identifiability analysis on the basis of the generalized sensitivity function, comparing then the results owith the ROUKF, using either synthetic or experimental data, to results obtained using reference parameters and to available measurements. Copyright © 2016 John Wiley & Sons, Ltd.

Vardenafil inhibiting parasympathetic function of tracheal smooth muscle.

Science.gov (United States)

Lee, Fei-Peng; Chao, Pin-Zhir; Wang, Hsing-Won

2018-07-01

Levitra, a phosphodiesterase-5 (PDE5) inhibitor, is the trade name of vardenafil. Nowadays, it is applied to treatment of erectile dysfunction. PDE5 inhibitors are employed to induce dilatation of the vascular smooth muscle. The effect of Levitra on impotency is well known; however, its effect on the tracheal smooth muscle has rarely been explored. When administered for sexual symptoms via oral intake or inhalation, Levitra might affect the trachea. This study assessed the effects of Levitra on isolated rat tracheal smooth muscle by examining its effect on resting tension of tracheal smooth muscle, contraction caused by 10 -6  M methacholine as a parasympathetic mimetic, and electrically induced tracheal smooth muscle contractions. The results showed that adding methacholine to the incubation medium caused the trachea to contract in a dose-dependent manner. Addition of Levitra at doses of 10 -5  M or above elicited a significant relaxation response to 10 -6  M methacholine-induced contraction. Levitra could inhibit electrical field stimulation-induced spike contraction. It alone had minimal effect on the basal tension of the trachea as the concentration increased. High concentrations of Levitra could inhibit parasympathetic function of the trachea. Levitra when administered via oral intake might reduce asthma attacks in impotent patients because it might inhibit parasympathetic function and reduce methacholine-induced contraction of the tracheal smooth muscle. Copyright © 2018. Published by Elsevier Taiwan LLC.

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. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Directory of Open Access Journals (Sweden)

Lijun Song

2018-01-01

Full Text Available The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA. But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

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

Parallel Kalman filter track fit based on vector classes

Energy Technology Data Exchange (ETDEWEB)

Kisel, Ivan [GSI Helmholtzzentrum fuer Schwerionenforschung GmbH (Germany); Kretz, Matthias [Kirchhoff-Institut fuer Physik, Ruprecht-Karls Universitaet, Heidelberg (Germany); Kulakov, Igor [Goethe-Universitaet, Frankfurt am Main (Germany); National Taras Shevchenko University, Kyiv (Ukraine)

2010-07-01

Modern high energy physics experiments have to process terabytes of input data produced in particle collisions. The core of the data reconstruction in high energy physics is the Kalman filter. Therefore, developing the fast Kalman filter algorithm, which uses maximum available power of modern processors, is important, in particular for initial selection of events interesting for the new physics. One of processors features, which can speed up the algorithm, is a SIMD instruction set, which allows to pack several data items in one register and operate on all of them in one go, thus achieving more operations per clock cycle. Therefore a flexible and useful interface, which uses the SIMD instruction set on different CPU and GPU processors architectures, has been realized as a vector classes library. The Kalman filter based track fitting algorithm has been implemented with use of the vector classes. Fitting quality tests show good results with the residuals equal to 49 {mu}m and 44 {mu}m for x and y track parameters and relative momentum resolution of 0.7%. The fitting time of 0.053 {mu}s per track has been achieved on Intel Xeon X5550 with 8 cores at 2.6 GHz by using in addition Intel Threading Building Blocks.

Cascaded Kalman and particle filters for photogrammetry based gyroscope drift and robot attitude estimation.

Science.gov (United States)

Sadaghzadeh N, Nargess; Poshtan, Javad; Wagner, Achim; Nordheimer, Eugen; Badreddin, Essameddin

2014-03-01

Based on a cascaded Kalman-Particle Filtering, gyroscope drift and robot attitude estimation method is proposed in this paper. Due to noisy and erroneous measurements of MEMS gyroscope, it is combined with Photogrammetry based vision navigation scenario. Quaternions kinematics and robot angular velocity dynamics with augmented drift dynamics of gyroscope are employed as system state space model. Nonlinear attitude kinematics, drift and robot angular movement dynamics each in 3 dimensions result in a nonlinear high dimensional system. To reduce the complexity, we propose a decomposition of system to cascaded subsystems and then design separate cascaded observers. This design leads to an easier tuning and more precise debugging from the perspective of programming and such a setting is well suited for a cooperative modular system with noticeably reduced computation time. Kalman Filtering (KF) is employed for the linear and Gaussian subsystem consisting of angular velocity and drift dynamics together with gyroscope measurement. The estimated angular velocity is utilized as input of the second Particle Filtering (PF) based observer in two scenarios of stochastic and deterministic inputs. Simulation results are provided to show the efficiency of the proposed method. Moreover, the experimental results based on data from a 3D MEMS IMU and a 3D camera system are used to demonstrate the efficiency of the method. © 2013 ISA Published by ISA All rights reserved.

Application and Optimization of Kalman Filter for Baseband Signal Processing of GPS Receivers

Directory of Open Access Journals (Sweden)

He Yanpin

2016-01-01

Full Text Available High sensitivity tracking in GPS receiver is required in many weak signal circumstances. The key of improving sensitivity is the optimization of the loop filter in tracking. As Kalman filter is the most optimized linear filter, it is used in many engineering fields. This article introduced the application of Kalman filter as the loop filter of the carrier tracking loop in GPS receiver, to improve tracking sensitivity. The traditional loop filter is replaced. Simulation results show that the new structure improves the tracking sensitivity by 6dB and can make the tracking loop more robust when the navigation signal is languishing. The optimization of theKalman filter is also analysed, which further improves the sensitivity by 4dB.

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

OpenAIRE

Mitchell, Lewis; Carrassi, Alberto

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 describe...

Estimating particle number size distributions from multi-instrument observations with Kalman Filtering

Energy Technology Data Exchange (ETDEWEB)

Viskari, T.

2012-07-01

Atmospheric aerosol particles have several important effects on the environment and human society. The exact impact of aerosol particles is largely determined by their particle size distributions. However, no single instrument is able to measure the whole range of the particle size distribution. Estimating a particle size distribution from multiple simultaneous measurements remains a challenge in aerosol physical research. Current methods to combine different measurements require assumptions concerning the overlapping measurement ranges and have difficulties in accounting for measurement uncertainties. In this thesis, Extended Kalman Filter (EKF) is presented as a promising method to estimate particle number size distributions from multiple simultaneous measurements. The particle number size distribution estimated by EKF includes information from prior particle number size distributions as propagated by a dynamical model and is based on the reliabilities of the applied information sources. Known physical processes and dynamically evolving error covariances constrain the estimate both over time and particle size. The method was tested with measurements from Differential Mobility Particle Sizer (DMPS), Aerodynamic Particle Sizer (APS) and nephelometer. The particle number concentration was chosen as the state of interest. The initial EKF implementation presented here includes simplifications, yet the results are positive and the estimate successfully incorporated information from the chosen instruments. For particle sizes smaller than 4 micrometers, the estimate fits the available measurements and smooths the particle number size distribution over both time and particle diameter. The estimate has difficulties with particles larger than 4 micrometers due to issues with both measurements and the dynamical model in that particle size range. The EKF implementation appears to reduce the impact of measurement noise on the estimate, but has a delayed reaction to sudden

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.

Estimation of single plane unbalance parameters of a rotor-bearing system using Kalman filtering based force estimation technique

Science.gov (United States)

Shrivastava, Akash; Mohanty, A. R.

2018-03-01

This paper proposes a model-based method to estimate single plane unbalance parameters (amplitude and phase angle) in a rotor using Kalman filter and recursive least square based input force estimation technique. Kalman filter based input force estimation technique requires state-space model and response measurements. A modified system equivalent reduction expansion process (SEREP) technique is employed to obtain a reduced-order model of the rotor system so that limited response measurements can be used. The method is demonstrated using numerical simulations on a rotor-disk-bearing system. Results are presented for different measurement sets including displacement, velocity, and rotational response. Effects of measurement noise level, filter parameters (process noise covariance and forgetting factor), and modeling error are also presented and it is observed that the unbalance parameter estimation is robust with respect to measurement noise.

Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver

Directory of Open Access Journals (Sweden)

Chan-Gun Lee

2016-06-01

Full Text Available Sensor fusion techniques have made a significant contribution to the success of the recently emerging mobile applications era because a variety of mobile applications operate based on multi-sensing information from the surrounding environment, such as navigation systems, fitness trackers, interactive virtual reality games, etc. For these applications, the accuracy of sensing information plays an important role to improve the user experience (UX quality, especially with gyroscopes and accelerometers. Therefore, in this paper, we proposed a novel mechanism to resolve the gyro drift problem, which negatively affects the accuracy of orientation computations in the indirect Kalman filter based sensor fusion. Our mechanism focuses on addressing the issues of external feedback loops and non-gyro error elements contained in the state vectors of an indirect Kalman filter. Moreover, the mechanism is implemented in the device-driver layer, providing lower process latency and transparency capabilities for the upper applications. These advances are relevant to millions of legacy applications since utilizing our mechanism does not require the existing applications to be re-programmed. The experimental results show that the root mean square errors (RMSE before and after applying our mechanism are significantly reduced from 6.3 × 10−1 to 5.3 × 10−7, respectively.

Solid-state lighting life prediction using extended Kalman filter

Energy Technology Data Exchange (ETDEWEB)

Lall, Pradeep [Auburn Univ., AL (United States); Wei, Junchao [Auburn Univ., AL (United States); Davis, Lynn [RTI International, Durham, NC (United States)

2013-07-16

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. The U.S. Department of Energy has made a long term commitment to advance the efficiency, understanding and development of solid-state lighting (SSL) and is making a strong push for the acceptance and use of SSL products to reduce overall energy consumption attributable to lighting. 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 SSL Luminaires from LM-80 test data. The TM-21 model uses an Arrhenius Equation with an Activation Energy, Pre-decay factor and Decay Rates. Several failure mechanisms may be active in a luminaire at a single time causing lumen depreciation. The underlying TM-21 Arrhenius 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, a Kalman Filter and Extended Kalman Filters have been used to develop a 70% Lumen Maintenance Life Prediction Model for a LEDs used in SSL luminaires. This model can be used to calculate acceleration factors, evaluate failure-probability and identify ALT methodologies for reducing test time. Ten-thousand hour LM

Kalman filter-based EM-optical sensor fusion for needle deflection estimation.

Science.gov (United States)

Jiang, Baichuan; Gao, Wenpeng; Kacher, Daniel; Nevo, Erez; Fetics, Barry; Lee, Thomas C; Jayender, Jagadeesan

2018-04-01

In many clinical procedures such as cryoablation that involves needle insertion, accurate placement of the needle's tip at the desired target is the major issue for optimizing the treatment and minimizing damage to the neighboring anatomy. However, due to the interaction force between the needle and tissue, considerable error in intraoperative tracking of the needle tip can be observed as needle deflects. In this paper, measurements data from an optical sensor at the needle base and a magnetic resonance (MR) gradient field-driven electromagnetic (EM) sensor placed 10 cm from the needle tip are used within a model-integrated Kalman filter-based sensor fusion scheme. Bending model-based estimations and EM-based direct estimation are used as the measurement vectors in the Kalman filter, thus establishing an online estimation approach. Static tip bending experiments show that the fusion method can reduce the mean error of the tip position estimation from 29.23 mm of the optical sensor-based approach to 3.15 mm of the fusion-based approach and from 39.96 to 6.90 mm, at the MRI isocenter and the MRI entrance, respectively. This work established a novel sensor fusion scheme that incorporates model information, which enables real-time tracking of needle deflection with MRI compatibility, in a free-hand operating setup.

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.

Observation Quality Control with a Robust Ensemble Kalman Filter

KAUST Repository

Roh, Soojin; Genton, Marc G.; Jun, Mikyoung; Szunyogh, Istvan; Hoteit, Ibrahim

2013-01-01

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.

A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images

Directory of Open Access Journals (Sweden)

Fernando Sedano

2014-12-01

Full Text Available A data assimilation method to produce complete temporal sequences of synthetic medium-resolution images is presented. The method implements a Kalman filter recursive algorithm that integrates medium and moderate resolution imagery. To demonstrate the approach, time series of 30-m spatial resolution NDVI images at 16-day time steps were generated using Landsat NDVI images and MODIS NDVI products at four sites with different ecosystems and land cover-land use dynamics. The results show that the time series of synthetic NDVI images captured seasonal land surface dynamics and maintained the spatial structure of the landscape at higher spatial resolution. The time series of synthetic medium-resolution NDVI images were validated within a Monte Carlo simulation framework. Normalized residuals decreased as the number of available observations increased, ranging from 0.2 to below 0.1. Residuals were also significantly lower for time series of synthetic NDVI images generated at combined recursion (smoothing than individually at forward and backward recursions (filtering. Conversely, the uncertainties of the synthetic images also decreased when the number of available observations increased and combined recursions were implemented.

An iterated cubature unscented Kalman filter for large-DoF systems identification with noisy data

Science.gov (United States)

Ghorbani, Esmaeil; Cha, Young-Jin

2018-04-01

Structural and mechanical system identification under dynamic loading has been an important research topic over the last three or four decades. Many Kalman-filtering-based approaches have been developed for linear and nonlinear systems. For example, to predict nonlinear systems, an unscented Kalman filter was applied. However, from extensive literature reviews, the unscented Kalman filter still showed weak performance on systems with large degrees of freedom. In this research, a modified unscented Kalman filter is proposed by integration of a cubature Kalman filter to improve the system identification performance of systems with large degrees of freedom. The novelty of this work lies on conjugating the unscented transform with the cubature integration concept to find a more accurate output from the transformation of the state vector and its related covariance matrix. To evaluate the proposed method, three different numerical models (i.e., the single degree-of-freedom Bouc-Wen model, the linear 3-degrees-of-freedom system, and the 10-degrees-of-freedom system) are investigated. To evaluate the robustness of the proposed method, high levels of noise in the measured response data are considered. The results show that the proposed method is significantly superior to the traditional UKF for noisy measured data in systems with large degrees of freedom.

Proofs and Techniques Useful for Deriving the Kalman Filter

National Research Council Canada - National Science Library

Koks, Don

2008-01-01

This note is a tutorial in matrix manipulation and the normal distribution of statistics, concepts that are important for deriving and analysing the Kalman Filter, a basic tool of signal processing...

Radionuclide release rate inversion of nuclear accidents in nuclear facility based on Kalman filter

International Nuclear Information System (INIS)

Tang Xiuhuan; Bao Lihong; Li Hua; Wan Junsheng

2014-01-01

The rapidly and continually back-calculating source term is important for nuclear emergency response. The Gaussian multi-puff atmospheric dispersion model was used to produce regional environment monitoring data virtually, and then a Kalman filter was designed to inverse radionuclide release rate of nuclear accidents in nuclear facility and the release rate tracking in real time was achieved. The results show that the Kalman filter combined with Gaussian multi-puff atmospheric dispersion model can successfully track the virtually stable, linear or nonlinear release rate after being iterated about 10 times. The standard error of inversion results increases with the true value. Meanwhile extended Kalman filter cannot inverse the height parameter of accident release as interceptive error is too large to converge. Kalman filter constructed from environment monitoring data and Gaussian multi-puff atmospheric dispersion model can be applied to source inversion in nuclear accident which is characterized by static height and position, short and continual release in nuclear facility. Hence it turns out to be an alternative source inversion method in nuclear emergency response. (authors)

A Hybrid Extended Kalman Filter as an Observer for a Pot-Electro-Magnetic Actuator

International Nuclear Information System (INIS)

Schmidt, Simon; Mercorelli, Paolo

2017-01-01

This paper deals with an application in which a hybrid extended Kalman Filter (HEKF) is used to estimate state variables in a U-shaped electro-magnetic actuator to be used in mechanical systems. In this context a hybrid Kalman Filter is the one which switches between different models. The paper proposes a hybrid model for an extended Kalman Filter to be used as an observer to estimate the state and to control the force of the actuator. Applications include position, velocity and force control in automotive, engine and manufacturing systems. This work is focused on the estimation of state variables of the actuator. Simulated results show the effectiveness of the proposed approach. (paper)

Fusion Kalman filtration with k-step delay sharing pattern

Directory of Open Access Journals (Sweden)

Duda Zdzisław

2015-09-01

Full Text Available A fusion hierarchical state filtration with k−step delay sharing pattern for a multisensor system is considered. A global state estimate depends on local state estimates determined by local nodes using local information. Local available information consists of local measurements and k−step delay global information - global estimate sent from a central node. Local estimates are transmitted to the central node to be fused. The synthesis of local and global filters is presented. It is shown that a fusion filtration with k−step delay sharing pattern is equivalent to the optimal centralized classical Kalman filtration when local measurements are transmitted to the center node and used to determine a global state estimate. It is proved that the k−step delay sharing pattern can reduce covariances of local state errors.

Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

Science.gov (United States)

Simon, Donald L.; Garg, Sanjay

2010-01-01

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy

Interacting Multiple Model (IMM Fifth-Degree Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking

Directory of Open Access Journals (Sweden)

Hua Liu

2017-06-01

Full Text Available For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF. The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF, the interacting multiple model cubature Kalman filter (IMMCKF and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF.

Interacting Multiple Model (IMM) Fifth-Degree Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking.

Science.gov (United States)

Liu, Hua; Wu, Wen

2017-06-13

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model cubature Kalman filter (IMMCKF) and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF).

Electrical resistance imaging of a time-varying interface in stratified flows using an unscented Kalman filter

International Nuclear Information System (INIS)

Ijaz, Umer Zeeshan; Khambampati, Anil Kumar; Kim, Kyung Youn; Chung, Soon Il; Kim, Sin

2008-01-01

In this paper, we estimate a time-varying interfacial boundary in stratified flows of two immiscible liquids using electrical resistance tomography. The interfacial boundary is approximated with front points spaced discretely along the interface. The design variables to be estimated are the locations of the front points, which are varying with the moving interface. The inverse problem is treated as a stochastic nonlinear state estimation problem with the nonstationary phase boundary (state) being estimated with the aid of an unscented Kalman filter. Numerical experiments are performed to evaluate the performance of an unscented Kalman filter. Specifically, a detailed analysis has been done on the effect of the number of front points and contrast ratio on the reconstruction performance. The reconstruction results show that an unscented Kalman filter is better suited for estimation in comparison to the conventional extended Kalman filter

Effects of contrast on smooth pursuit eye movements.

Science.gov (United States)

Spering, Miriam; Kerzel, Dirk; Braun, Doris I; Hawken, Michael J; Gegenfurtner, Karl R

2005-05-20

It is well known that moving stimuli can appear to move more slowly when contrast is reduced (P. Thompson, 1982). Here we address the question whether changes in stimulus contrast also affect smooth pursuit eye movements. Subjects were asked to smoothly track a moving Gabor patch. Targets varied in velocity (1, 8, and 15 deg/s), spatial frequency (0.1, 1, 4, and 8 c/deg), and contrast, ranging from just below individual thresholds to maximum contrast. Results show that smooth pursuit eye velocity gain rose significantly with increasing contrast. Below a contrast level of two to three times threshold, pursuit gain, acceleration, latency, and positional accuracy were severely impaired. Therefore, the smooth pursuit motor response shows the same kind of slowing at low contrast that was demonstrated in previous studies on perception.

Investigation, development, and application of optimal output feedback theory. Volume 3: The relationship between dynamic compensators and observers and Kalman filters

Science.gov (United States)

Broussard, John R.

1987-01-01

Relationships between observers, Kalman Filters and dynamic compensators using feedforward control theory are investigated. In particular, the relationship, if any, between the dynamic compensator state and linear functions of a discrete plane state are investigated. It is shown that, in steady state, a dynamic compensator driven by the plant output can be expressed as the sum of two terms. The first term is a linear combination of the plant state. The second term depends on plant and measurement noise, and the plant control. Thus, the state of the dynamic compensator can be expressed as an estimator of the first term with additive error given by the second term. Conditions under which a dynamic compensator is a Kalman filter are presented, and reduced-order optimal estimaters are investigated.

Kalman filter based fault diagnosis of networked control system with white noise

Institute of Scientific and Technical Information of China (English)

Yanwei WANG; Ying ZHENG

2005-01-01

The networked control system NCS is regarded as a sampled control system with output time-variant delay.White noise is considered in the model construction of NCS.By using the Kalman filter theory to compute the filter parameters,a Kalman filter is constructed for this NCS.By comparing the output of the filter and the practical system,a residual is generated to diagnose the sensor faults and the actuator faults.Finally,an example is given to show the feasibility of the approach.

Identification and simulation for steam generator water level based on Kalman Filter

International Nuclear Information System (INIS)

Deng Chen; Zhang Qinshun

2008-01-01

In order to effectively control the water level of the steam generator (SG), this paper has set about the state-observer theory in modern control and put forward a method to detect the 'false water level' based on Kalman Filter. Kalman Filter is a efficient tool to estimate state-variable by measured value including noise. For heavy measurement noise of steam flow, constructing a 'false water level' observer by Kalman Filter could availably obtain state variable of 'false water level'. The simulation computing for the dynamics characteristic of nuclear SG water level process under several typically running power was implemented by employing the simulation model. The result shows that the simulation model accurately identifies the 'false water level' produced in the reverse thermal-dynamic effects of nuclear SG water level process. The simulation model can realize the precise analysis of dynamics characteristic for the nuclear SG water level process. It can provide a kind of new ideas for the 'false water level' detecting of SG. (authors)

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

International Nuclear Information System (INIS)

Lee, Jung Keun

2015-01-01

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

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.

Adaptive Unscented Kalman Filter using Maximum Likelihood Estimation

DEFF Research Database (Denmark)

Mahmoudi, Zeinab; Poulsen, Niels Kjølstad; Madsen, Henrik

2017-01-01

The purpose of this study is to develop an adaptive unscented Kalman filter (UKF) by tuning the measurement noise covariance. We use the maximum likelihood estimation (MLE) and the covariance matching (CM) method to estimate the noise covariance. The multi-step prediction errors generated...

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.

Application Of Kalman Filter In Navigation Process Of Automated Guided Vehicles

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Śmieszek Mirosław

2015-09-01

Full Text Available In the paper an example of application of the Kalman filtering in the navigation process of automatically guided vehicles was presented. The basis for determining the position of automatically guided vehicles is odometry – the navigation calculation. This method of determining the position of a vehicle is affected by many errors. In order to eliminate these errors, in modern vehicles additional systems to increase accuracy in determining the position of a vehicle are used. In the latest navigation systems during route and position adjustments the probabilistic methods are used. The most frequently applied are Kalman filters.

Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing

Directory of Open Access Journals (Sweden)

Yongliang Sun

2013-11-01

Full Text Available A Kalman/map filtering (KMF-aided fast normalized cross correlation (FNCC-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.

Kalman/Map filtering-aided fast normalized cross correlation-based Wi-Fi fingerprinting location sensing.

Science.gov (United States)

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-11-13

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.

Smooth massless limit of field theories

International Nuclear Information System (INIS)

Fronsdal, C.

1980-01-01

The massless limit of Fierz-Pauli field theories, describing fields with fixed mass and spin interacting with external sources, is examined. Results are obtained for spins, 1, 3/2, 2 and 3 using conventional models, and then for all half-integral spins in a relatively model-independent manner. It is found that the massless limit is smooth provided that the sources satisfy certain conditions. In the massless limit these conditions reduce to the conservation laws required by internal consistency of massless field theory. Smoothness simply requires that quantities that vanish in the massless case approach zero in a certain well-defined manner. (orig.)

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.

Rotation speed measurement for turbine governor: torsion filtering by using Kalman filter

International Nuclear Information System (INIS)

Houry, M.P.; Bourles, H.

1995-11-01

The rotation speed of a turbogenerator is disturbed by its shaft torsion. Obtaining a filtered measure of this sped a problem of a great practical importance for turbine governor. A good filtering of this speed must meet two requirements: it must cut frequencies of the shaft torsion oscillation and it must not reduce or delay the signal in the pass-band. i.e. at lower frequencies. At Electricite de France, the speed measure is used to set in motion the fast valving system as quickly as possible, after a short circuit close to the unit (to contribute to the stability) or after an islanding (to quickly reach a balance with the house load). It is difficult to satisfy these two requirements by using conventional filtering methods. The standard solution consists in a first order filter: at Electricite de France, its time constant is equal to 80 ms; We have decided to improve this filtering by designing a new filter which cuts the frequencies of the shaft torsion oscillation without reducing the bandwidth of the speed measure. If one uses conventional methods to obtain a band-stop filter (for instance a Butterworth, a Chebyshev or an elliptic band-stop filter),it is easy to obtain the desired magnitude but not a phase near zero in the whole pass-band. Therefore, we have chosen to design the filter by using Kalman's theory. The measurement noise is modeled as a colored one, generated by a very lightly damped system driven by a white noise. The resulting Kalman filter is an effective band-stop filter, whose phase nicely remains near zero in the whole pass-band. (authors). 13 refs., 12 figs

Real-time noise reduction for Mössbauer spectroscopy through online implementation of a modified Kalman filter

Energy Technology Data Exchange (ETDEWEB)

Abrecht, David G., E-mail: david.abrecht@pnnl.gov [National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352 (United States); Schwantes, Jon M. [National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352 (United States); Kukkadapu, Ravi K. [Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352 (United States); McDonald, Benjamin S.; Eiden, Gregory C.; Sweet, Lucas E. [National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352 (United States)

2015-02-11

Spectrum-processing software that incorporates a Gaussian smoothing kernel within the statistics of first-order Kalman filtration has been developed to provide cross-channel spectral noise reduction for increased real-time signal-to-noise ratios for Mössbauer spectroscopy. The filter was optimized for the breadth of the Gaussian using the Mössbauer spectrum of natural iron foil, and comparisons among the peak broadening, signal-to-noise ratios, and shifts in the calculated hyperfine parameters are presented. The results of optimization give a maximum improvement in the signal-to-noise ratio of 51.1% over the unfiltered spectrum at a Gaussian breadth of 27 channels, or 2.5% of the total spectrum width. The full-width half-maximum of the spectrum peaks showed an increase of 19.6% at this optimum point, indicating a relatively weak increase in the peak broadening relative to the signal enhancement, leading to an overall increase in the observable signal. Calculations of the hyperfine parameters showed that no statistically significant deviations were introduced from the application of the filter, confirming the utility of this filter for spectroscopy applications.

Can Reduced-Step Polishers Be as Effective as Multiple-Step Polishers in Enhancing Surface Smoothness?

Science.gov (United States)

Kemaloglu, Hande; Karacolak, Gamze; Turkun, L Sebnem

2017-02-01

The aim of this study was to evaluate the effects of various finishing and polishing systems on the final surface roughness of a resin composite. Hypotheses tested were: (1) reduced-step polishing systems are as effective as multiple-step systems on reducing the surface roughness of a resin composite and (2) the number of application steps in an F/P system has no effect on reducing surface roughness. Ninety discs of a nano-hybrid resin composite were fabricated and divided into nine groups (n = 10). Except the control, all of the specimens were roughened prior to be polished by: Enamel Plus Shiny, Venus Supra, One-gloss, Sof-Lex Wheels, Super-Snap, Enhance/PoGo, Clearfil Twist Dia, and rubber cups. The surface roughness was measured and the surfaces were examined under scanning electron microscope. Results were analyzed with analysis of variance and Holm-Sidak's multiple comparisons test (p One-gloss, Enamel Plus Shiny, and Venus Supra groups. (1) The number of application steps has no effect on the performance of F/P systems. (2) Reduced-step polishers used after a finisher can be preferable to multiple-step systems when used on nanohybrid resin composites. (3) The effect of F/P systems on surface roughness seems to be material-dependent rather than instrument- or system-dependent. Reduced-step systems used after a prepolisher can be an acceptable alternative to multiple-step systems on enhancing the surface smoothness of a nanohybrid composite; however, their effectiveness depends on the materials' properties. (J Esthet Restor Dent 29:31-40, 2017). © 2016 Wiley Periodicals, Inc.

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.

Kalman Filtering for Discrete Stochastic Systems with Multiplicative Noises and Random Two-Step Sensor Delays

Directory of Open Access Journals (Sweden)

Dongyan Chen

2015-01-01

Full Text Available This paper is concerned with the optimal Kalman filtering problem for a class of discrete stochastic systems with multiplicative noises and random two-step sensor delays. Three Bernoulli distributed random variables with known conditional probabilities are introduced to characterize the phenomena of the random two-step sensor delays which may happen during the data transmission. By using the state augmentation approach and innovation analysis technique, an optimal Kalman filter is constructed for the augmented system in the sense of the minimum mean square error (MMSE. Subsequently, the optimal Kalman filtering is derived for corresponding augmented system in initial instants. Finally, a simulation example is provided to demonstrate the feasibility and effectiveness of the proposed filtering method.

Phase Center Interpolation Algorithm for Airborne GPS through the Kalman Filter

Directory of Open Access Journals (Sweden)

Edson A. Mitishita

2005-12-01

Full Text Available The aerial triangulation is a fundamental step in any photogrammetric project. The surveying of the traditional control points, depending on region to be mapped, still has a high cost. The distribution of control points at the block, and its positional quality, influence directly in the resulting precisions of the aero triangulation processing. The airborne GPS technique has as key objectives cost reduction and quality improvement of the ground control in the modern photogrammetric projects. Nowadays, in Brazil, the greatest photogrammetric companies are acquiring airborne GPS systems, but those systems are usually presenting difficulties in the operation, due to the need of human resources for the operation, because of the high technology involved. Inside the airborne GPS technique, one of the fundamental steps is the interpolation of the position of the phase center of the GPS antenna, in the photo shot instant. Traditionally, low degree polynomials are used, but recent studies show that those polynomials is reduced in turbulent flights, which are quite common, mainly in great scales flights. This paper has as objective to present a solution for that problem, through an algorithm based on the Kalman Filter, which takes into account the dynamic aspect of the problem. At the end of the paper, the results of a comparison between experiments done with the proposed methodology and a common linear interpolator are shown. These results show a significant accuracy gain at the procedure of linear interpolation, when the Kalman filter is used.

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

Science.gov (United States)

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.

Predicting Breeding Values in Animals by Kalman Filter

DEFF Research Database (Denmark)

Karacaoren, B; Janss, L L G; Kadarmideen, H N

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...

The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.

Science.gov (United States)

Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck

2016-07-16

This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.

A reduced-order filtering approach for 3D dynamical electrical impedance tomography

International Nuclear Information System (INIS)

Voutilainen, A; Lehikoinen, A; Vauhkonen, M; Kaipio, J P

2011-01-01

Recently, it has been shown that the state estimation approach to process tomography can provide estimates that are significantly better than (a sequence of) conventional stationary snapshot estimates. One of the main obstacles of the adoption of the recursive state estimation algorithms, most commonly different versions of the Kalman filter, is the computational complexity. This is due to both the required large dimension for the state variable and the need to use iterative versions of the Kalman filter in such cases in which there are large contrasts or varying background. In this paper, we propose to use a reduced-order representation for the state variable. In particular, we propose to use the proper orthogonal decomposition-related basis for the state. We consider a simulation study with fluctuating background conductivity, and, in particular, with fluctuating contact impedances. We compare the proposed approach to three different versions of the Kalman filter having different computational complexities. We show that this approach allows the reduction of the dimension of the problem approximately by an order of magnitude and yields essentially as accurate estimates as the most accurate traditional Kalman filter version, the iterated extended Kalman filter

Design of Low-Cost Vehicle Roll Angle Estimator Based on Kalman Filters and an Iot Architecture.

Science.gov (United States)

Garcia Guzman, Javier; Prieto Gonzalez, Lisardo; Pajares Redondo, Jonatan; Sanz Sanchez, Susana; Boada, Beatriz L

2018-06-03

In recent years, there have been many advances in vehicle technologies based on the efficient use of real-time data provided by embedded sensors. Some of these technologies can help you avoid or reduce the severity of a crash such as the Roll Stability Control (RSC) systems for commercial vehicles. In RSC, several critical variables to consider such as sideslip or roll angle can only be directly measured using expensive equipment. These kind of devices would increase the price of commercial vehicles. Nevertheless, sideslip or roll angle or values can be estimated using MEMS sensors in combination with data fusion algorithms. The objectives stated for this research work consist of integrating roll angle estimators based on Linear and Unscented Kalman filters to evaluate the precision of the results obtained and determining the fulfillment of the hard real-time processing constraints to embed this kind of estimators in IoT architectures based on low-cost equipment able to be deployed in commercial vehicles. An experimental testbed composed of a van with two sets of low-cost kits was set up, the first one including a Raspberry Pi 3 Model B, and the other having an Intel Edison System on Chip. This experimental environment was tested under different conditions for comparison. The results obtained from low-cost experimental kits, based on IoT architectures and including estimators based on Kalman filters, provide accurate roll angle estimation. Also, these results show that the processing time to get the data and execute the estimations based on Kalman Filters fulfill hard real time constraints.

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

Sensor failure detection in dynamical systems by Kalman filtering methodology

International Nuclear Information System (INIS)

Ciftcioglu, O.

1991-03-01

Design of a sensor failure detection system by Kalman filtering methodology is described. The method models the process systems in state-space form, the information on each state being provided by relevant sensors present in the process system. Since the measured states are usually subject to noise, the estimation of the states optimally is an essential requirement. To this end the detection system comprises Kalman estimation filters, the number of which is equal to the number of states concerned. The estimated state of a particular signal in each filter is compared with the corresponding measured signal and difference beyond a predetermined bound is identified as failure, the sensor being identified/isolated as faulty. (author). 19 refs.; 8 figs.; 1 tab

Ensemble-marginalized Kalman filter for linear time-dependent PDEs with noisy boundary conditions: Application to heat transfer in building walls

KAUST Repository

Iglesias, Marco

2017-11-26

In this work, we present the ensemble-marginalized Kalman filter (EnMKF), a sequential algorithm analogous to our previously proposed approach [1,2], for estimating the state and parameters of linear parabolic partial differential equations in initial-boundary value problems when the boundary data are noisy. We apply EnMKF to infer the thermal properties of building walls and to estimate the corresponding heat flux from real and synthetic data. Compared with a modified Ensemble Kalman Filter (EnKF) that is not marginalized, EnMKF reduces the bias error, avoids the collapse of the ensemble without needing to add inflation, and converges to the mean field posterior using $50\\\\%$ or less of the ensemble size required by EnKF. According to our results, the marginalization technique in EnMKF is key to performance improvement with smaller ensembles at any fixed time.

Accurate human limb angle measurement: sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering

Science.gov (United States)

Olivares, A.; Górriz, J. M.; Ramírez, J.; Olivares, G.

2011-02-01

Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed.

Accurate human limb angle measurement: sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering

International Nuclear Information System (INIS)

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

2011-01-01

Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed

Distributed Cerebral Blood Flow estimation using a spatiotemporal hemodynamic response model and a Kalman-like Filter approach

KAUST Repository

Belkhatir, Zehor

2015-11-23

This paper discusses the estimation of distributed Cerebral Blood Flow (CBF) using spatiotemporal traveling wave model. We consider a damped wave partial differential equation that describes a physiological relationship between the blood mass density and the CBF. The spatiotemporal model is reduced to a finite dimensional system using a cubic b-spline continuous Galerkin method. A Kalman Filter with Unknown Inputs without Direct Feedthrough (KF-UI-WDF) is applied on the obtained reduced differential model to estimate the source term which is the CBF scaled by a factor. Numerical results showing the performances of the adopted estimator are provided.

Potential fire detection based on Kalman-driven change detection

CSIR Research Space (South Africa)

Van Den Bergh, F

2009-07-01

Full Text Available A new active fire event detection algorithm for data collected with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor, based on the extended Kalman filter, is introduced. Instead of using the observed temperatures of the spatial...

Kalman filter approach for uncertainty quantification in time-resolved laser-induced incandescence.

Science.gov (United States)

Hadwin, Paul J; Sipkens, Timothy A; Thomson, Kevin A; Liu, Fengshan; Daun, Kyle J

2018-03-01

Time-resolved laser-induced incandescence (TiRe-LII) data can be used to infer spatially and temporally resolved volume fractions and primary particle size distributions of soot-laden aerosols, but these estimates are corrupted by measurement noise as well as uncertainties in the spectroscopic and heat transfer submodels used to interpret the data. Estimates of the temperature, concentration, and size distribution of soot primary particles within a sample aerosol are typically made by nonlinear regression of modeled spectral incandescence decay, or effective temperature decay, to experimental data. In this work, we employ nonstationary Bayesian estimation techniques to infer aerosol properties from simulated and experimental LII signals, specifically the extended Kalman filter and Schmidt-Kalman filter. These techniques exploit the time-varying nature of both the measurements and the models, and they reveal how uncertainty in the estimates computed from TiRe-LII data evolves over time. Both techniques perform better when compared with standard deterministic estimates; however, we demonstrate that the Schmidt-Kalman filter produces more realistic uncertainty estimates.

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.

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.

Multivariate localization methods for ensemble Kalman filtering

KAUST Repository

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

2015-01-01

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 (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.

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.

Neutron flux filtration using Kalman filter

International Nuclear Information System (INIS)

Urcikan, Marian

2005-01-01

In the course of the WWER-440 start-up procedure the time dependent reactivity is determined from the measured ionization chamber signal by inverse kinetic method. Due to the random nature of the fission process and random nature the detection process the measured ionization chamber signal contains certain noise content. To minimize the unwonted noise on measured reactivity one of the possibility method is utilization Kalman filter, based on a stochastic model of reactor system (Author)

Optical smoothing of laser imprinting in planar-target experiments on OMEGA EP using multi-FM 1-D smoothing by spectral dispersion

Energy Technology Data Exchange (ETDEWEB)

Hohenberger, M., E-mail: mhoh@lle.rochester.edu; Shvydky, A.; Marozas, J. A.; Bonino, M. J.; Canning, D.; Collins, T. J. B.; Dorrer, C.; Kessler, T. J.; Kruschwitz, B. E.; McKenty, P. W.; Regan, S. P.; Sangster, T. C.; Zuegel, J. D. [Laboratory for Laser Energetics, University of Rochester, 250 East River Road Rochester, Rochester, New York 14623 (United States); Fiksel, G. [Nuclear Engineering and Radiological Sciences (NERS), University of Michigan, Ann Arbor, Michigan 48109 (United States); Meyerhofer, D. D. [Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States)

2016-09-15

Direct-drive ignition on the National Ignition Facility (NIF) requires single-beam smoothing to minimize imprinting of laser nonuniformities that can negatively affect implosion performance. One-dimensional, multi-FM smoothing by spectral dispersion (SSD) has been proposed to provide the required smoothing [Marozas et al., Bull. Am. Phys. Soc. 55, 294 (2010)]. A prototype multi-FM SSD system has been integrated into the NIF-like beamline of the OMEGA EP Laser System. Experiments have been performed to verify the smoothing performance by measuring Rayleigh–Taylor growth rates in planar targets of laser-imprinted and preimposed surface modulations. Multi-FM 1-D SSD has been observed to reduce imprint levels by ∼50% compared to the nominal OMEGA EP SSD system. The experimental results are in agreement with 2-D DRACO simulations using realistic, time-dependent far-field spot-intensity calculations that emulate the effect of SSD.

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

International Nuclear Information System (INIS)

Huang Jin-Wang; Feng Jiu-Chao

2014-01-01

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks (WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter (CKPF) is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter (CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter (UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF. (general)

State and force observers based on multibody models and the indirect Kalman filter

Science.gov (United States)

Sanjurjo, Emilio; Dopico, Daniel; Luaces, Alberto; Naya, Miguel Ángel

2018-06-01

The aim of this work is to present two new methods to provide state observers by combining multibody simulations with indirect extended Kalman filters. One of the methods presented provides also input force estimation. The observers have been applied to two mechanism with four different sensor configurations, and compared to other multibody-based observers found in the literature to evaluate their behavior, namely, the unscented Kalman filter (UKF), and the indirect extended Kalman filter with simplified Jacobians (errorEKF). The new methods have some more computational cost than the errorEKF, but still much less than the UKF. Regarding their accuracy, both are better than the errorEKF. The method with input force estimation outperforms also the UKF, while the method without force estimation achieves results almost identical to those of the UKF. All the methods have been implemented as a reusable MATLAB® toolkit which has been released as Open Source in https://github.com/MBDS/mbde-matlab.

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.

Comparison between GSTAR and GSTAR-Kalman Filter models on inflation rate forecasting in East Java

Science.gov (United States)

Rahma Prillantika, Jessica; Apriliani, Erna; Wahyuningsih, Nuri

2018-03-01

Up to now, we often find data which have correlation between time and location. This data also known as spatial data. Inflation rate is one type of spatial data because it is not only related to the events of the previous time, but also has relevance to the other location or elsewhere. In this research, we do comparison between GSTAR model and GSTAR-Kalman Filter to get prediction which have small error rate. Kalman Filter is one estimator that estimates state changes due to noise from white noise. The final result shows that Kalman Filter is able to improve the GSTAR forecast result. This is shown through simulation results in the form of graphs and clarified with smaller RMSE values.

L70 life prediction for solid state lighting using Kalman Filter and Extended Kalman Filter based models

Energy Technology Data Exchange (ETDEWEB)

Lall, Pradeep; Wei, Junchao; Davis, Lynn

2013-08-08

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. The measured state variable has been related to the underlying damage using physics-based models. Life

Acoustic cardiac signals analysis: a Kalman filter–based approach

Directory of Open Access Journals (Sweden)

Salleh SH

2012-06-01

Full Text Available Sheik Hussain Salleh,1 Hadrina Sheik Hussain,2 Tan Tian Swee,2 Chee-Ming Ting,2 Alias Mohd Noor,2 Surasak Pipatsart,3 Jalil Ali,4 Preecha P Yupapin31Department of Biomedical Instrumentation and Signal Processing, Universiti Teknologi Malaysia, Skudai, Malaysia; 2Centre for Biomedical Engineering Transportation Research Alliance, Universiti Teknologi Malaysia, Johor Bahru, Malaysia; 3Nanoscale Science and Engineering Research Alliance, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand; 4Institute of Advanced Photonics Science, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaAbstract: 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

The Kalman filter for the pedologist's tool kit

NARCIS (Netherlands)

Webster, R.; Heuvelink, G.B.M.

2006-01-01

The Kalman filter is a tool designed primarily to estimate the values of the `state¿ of a dynamic system in time. There are two main equations. These are the state equation, which describes the behaviour of the state over time, and the measurement equation, which describes at what times and in what

Analysis and comparison of extended and unscented Kalman filtering methods for spacecraft attitude determination

OpenAIRE

Diaz, Orlando X.

2010-01-01

Approved for public release; distribution is unlimited Two methods of estimating the attitude position of a spacecraft are examined in this thesis: the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). In particular, the UnScented QUaternion Estimator (USQUE) derived from [4] is implemented into a spacecraft model. For generalizations about the each of the filters, a simple problem is initially solved. These solutions display typical characteristics of each filter type. T...

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.

Bayesian fault detection and isolation using Field Kalman Filter

Science.gov (United States)

Baranowski, Jerzy; Bania, Piotr; Prasad, Indrajeet; Cong, Tian

2017-12-01

Fault detection and isolation is crucial for the efficient operation and safety of any industrial process. There is a variety of methods from all areas of data analysis employed to solve this kind of task, such as Bayesian reasoning and Kalman filter. In this paper, the authors use a discrete Field Kalman Filter (FKF) to detect and recognize faulty conditions in a system. The proposed approach, devised for stochastic linear systems, allows for analysis of faults that can be expressed both as parameter and disturbance variations. This approach is formulated for the situations when the fault catalog is known, resulting in the algorithm allowing estimation of probability values. Additionally, a variant of algorithm with greater numerical robustness is presented, based on computation of logarithmic odds. Proposed algorithm operation is illustrated with numerical examples, and both its merits and limitations are critically discussed and compared with traditional EKF.

Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?

Directory of Open Access Journals (Sweden)

Simionescu Mihaela

2015-06-01

Full Text Available This paper brings to light an economic problem that frequently appears in practice: For the same variable, more alternative forecasts are proposed, yet the decision-making process requires the use of a single prediction. Therefore, a forecast assessment is necessary to select the best prediction. The aim of this research is to propose some strategies for improving the unemployment rate forecast in Romania by conducting a comparative accuracy analysis of unemployment rate forecasts based on two quantitative methods: Kalman filter and vector-auto-regressive (VAR models. The first method considers the evolution of unemployment components, while the VAR model takes into account the interdependencies between the unemployment rate and the inflation rate. According to the Granger causality test, the inflation rate in the first difference is a cause of the unemployment rate in the first difference, these data sets being stationary. For the unemployment rate forecasts for 2010-2012 in Romania, the VAR models (in all variants of VAR simulations determined more accurate predictions than Kalman filter based on two state space models for all accuracy measures. According to mean absolute scaled error, the dynamic-stochastic simulations used in predicting unemployment based on the VAR model are the most accurate. Another strategy for improving the initial forecasts based on the Kalman filter used the adjusted unemployment data transformed by the application of the Hodrick-Prescott filter. However, the use of VAR models rather than different variants of the Kalman filter methods remains the best strategy in improving the quality of the unemployment rate forecast in Romania. The explanation of these results is related to the fact that the interaction of unemployment with inflation provides useful information for predictions of the evolution of unemployment related to its components (i.e., natural unemployment and cyclical component.

Linear discrete-time state space realization of a modified quadruple tank system with state estimation using Kalman filter

DEFF Research Database (Denmark)

Mohd. Azam, Sazuan Nazrah

2017-01-01

In this paper, we used the modified quadruple tank system that represents a multi-input-multi-output (MIMO) system as an example to present the realization of a linear discrete-time state space model and to obtain the state estimation using Kalman filter in a methodical mannered. First, an existing...... part of the Kalman filter is used to estimates the current state, based on the model and the measurements. The static and dynamic Kalman filter is compared and all results is demonstrated through simulations....

Kalman Filter Tracking on Parallel Architectures

International Nuclear Information System (INIS)

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

Use of Kalman filter methods in analysis of in-pile LMFBR accident simulations

International Nuclear Information System (INIS)

Meek, C.C.; Doerner, R.C.

1983-01-01

Kalman filter methodology has been applied to inpile liquid-metal fast breeder reactor simulation experiments to obtain estimates of the fuel-clad thermal gap conductance. A transient lumped parameter model of the experiment is developed. An optimal estimate of the state vector chosen to characterize the experiment is obtained through the use of the Kalman filter. From this estimate, the fuel-clad thermal gap conductance is calculated as a function of time into the test and axial position along the length of the fuel pin

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.

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.

Improving Artificial Neural Network Forecasts with Kalman Filtering ...

African Journals Online (AJOL)

In this paper, we examine the use of the artificial neural network method as a forecasting technique in financial time series and the application of a Kalman filter algorithm to improve the accuracy of the model. Forecasting accuracy criteria are used to compare the two models over different set of data from different companies ...

Smooth manifolds

CERN Document Server

Sinha, Rajnikant

2014-01-01

This book offers an introduction to the theory of smooth manifolds, helping students to familiarize themselves with the tools they will need for mathematical research on smooth manifolds and differential geometry. The book primarily focuses on topics concerning differential manifolds, tangent spaces, multivariable differential calculus, topological properties of smooth manifolds, embedded submanifolds, Sard’s theorem and Whitney embedding theorem. It is clearly structured, amply illustrated and includes solved examples for all concepts discussed. Several difficult theorems have been broken into many lemmas and notes (equivalent to sub-lemmas) to enhance the readability of the book. Further, once a concept has been introduced, it reoccurs throughout the book to ensure comprehension. Rank theorem, a vital aspect of smooth manifolds theory, occurs in many manifestations, including rank theorem for Euclidean space and global rank theorem. Though primarily intended for graduate students of mathematics, the book ...

Implicit Kalman filter algorithm for nuclear reactor analysis

International Nuclear Information System (INIS)

Hassberger, J.A.; Lee, J.C.

1986-01-01

Artificial intelligence (AI) is currently the hot topic in nuclear power plant diagnostics and control. Recently, researchers have considered the use of simulation as knowledge in which faster than real-time best-estimate simulations based on first principles are tightly coupled with AI systems for analyzing power plant transients on-line. On-line simulations can be improved through a Kalman filter, a mathematical technique for obtaining the optimal estimate of a system state given the information contained in the equations of system dynamics and measurements made on the system. Filtering can be used to systemically adjust parameters of a low-order simulation model to obtain reasonable agreement between the model and actual plant dynamics. The authors present here a general Kalman filtering algorithm that derives its information of system dynamics implicitly and naturally from the discrete time step-series of state estimates available from a simulation program. Previous research has demonstrated that models adjusted on past data can be coupled with an intelligent controller to predict the future time-course of plant transients

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.

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.

An Adaptive Estimation of Forecast Error Covariance Parameters for Kalman Filtering Data Assimilation

Institute of Scientific and Technical Information of China (English)

Xiaogu ZHENG

2009-01-01

An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model (Burgers' equation model) is used to demonstrate the efficacy of the proposed approach.

Algoritma Filter Kalman untuk Menghaluskan Data Pengukuran

OpenAIRE

Rudiyanto; Setiawan, Budi Indra; Saptomo, Satyanto Krido

2006-01-01

The objective of this paper is to apply a simple algorithm of Kalman Filter, wich is know as noise data filtering. The computer program was written in Macro Visual Basic in MS Exel. Testings were carried out on available temperature, Water level and force data and then were comared with the mooving average method. The result shows that the algorithm performed better and lesser deviation than the mooving average.

Investigation of angular and axial smoothing of PET data

International Nuclear Information System (INIS)

Daube-Witherspoon, M.E.; Carson, R.E.

1996-01-01

Radial filtering of emission and transmission data is routinely performed in PET during reconstruction in order to reduce image noise. Angular smoothing is not typically done, due to the introduction of a non-uniform resolution loss; axial filtering is also not usually performed on data acquired in 2D mode. The goal of this paper was to assess the effects of angular and axial smoothing on noise and resolution. Angular and axial smoothing was incorporated into the reconstruction process on the Scanditronix PC2048-15B brain PET scanner. In-plane spatial resolution and noise reduction were measured for different amounts of radial and angular smoothing. For radial positions away from the center of the scanner, noise reduction and degraded tangential resolution with no loss of radial resolution were seen. Near the center, no resolution loss was observed, but there was also no reduction in noise for angular filters up to a 7 degrees FWHM. These results can be understood by considering the combined effects of smoothing projections across rows (angles) and then summing (backprojecting). Thus, angular smoothing is not optimal due to its anisotropic noise reduction and resolution degradation properties. However, uniform noise reduction comparable to that seen with radial filtering can be achieved with axial smoothing of transmission data. The axial results suggest that combined radial and axial transmission smoothing could lead to improved noise characteristics with more isotropic resolution degradation

Implementation of a reduced order Kalman filter to assimilate ocean color data into a coupled physical-biochemical model of the North Aegean Sea.

Science.gov (United States)

Kalaroni, Sofia; Tsiaras, Kostas; Economou-Amilli, Athena; Petihakis, George; Politikos, Dimitrios; Triantafyllou, George

2013-04-01

Within the framework of the European project OPEC (Operational Ecology), a data assimilation system was implemented to describe chlorophyll-a concentrations of the North Aegean, as well the impact on the European anchovy (Engraulis encrasicolus) biomass distribution provided by a bioenergetics model, related to the density of three low trophic level functional groups of zooplankton (heterotrophic flagellates, microzooplankton and mesozooplankton). The three-dimensional hydrodynamic-biogeochemical model comprises two on-line coupled sub-models: the Princeton Ocean Model (POM) and the European Regional Seas Ecosystem Model (ERSEM). The assimilation scheme is based on the Singular Evolutive Extended Kalman (SEEK) filter and its variant that uses a fixed correction base (SFEK). For the initialization, SEEK filter uses a reduced order error covariance matrix provided by the dominant Empirical Orthogonal Functions (EOF) of model. The assimilation experiments were performed for year 2003 using SeaWiFS chlorophyll-a data during which the physical model uses the atmospheric forcing obtained from the regional climate model HIRHAM5. The assimilation system is validated by assessing the relevance of the system in fitting the data, the impact of the assimilation on non-observed biochemical parameters and the overall quality of the forecasts.

Neuromorphic Kalman filter implementation in IBM’s TrueNorth

Science.gov (United States)

Carney, R.; Bouchard, K.; Calafiura, P.; Clark, D.; Donofrio, D.; Garcia-Sciveres, M.; Livezey, J.

2017-10-01

Following the advent of a post-Moore’s law field of computation, novel architectures continue to emerge. With composite, multi-million connection neuromorphic chips like IBM’s TrueNorth, neural engineering has now become a feasible technology in this novel computing paradigm. High Energy Physics experiments are continuously exploring new methods of computation and data handling, including neuromorphic, to support the growing challenges of the field and be prepared for future commodity computing trends. This work details the first instance of a Kalman filter implementation in IBM’s neuromorphic architecture, TrueNorth, for both parallel and serial spike trains. The implementation is tested on multiple simulated systems and its performance is evaluated with respect to an equivalent non-spiking Kalman filter. The limits of the implementation are explored whilst varying the size of weight and threshold registers, the number of spikes used to encode a state, size of neuron block for spatial encoding, and neuron potential reset schemes.

The use of the Kalman filter in the automated segmentation of EIT lung images

International Nuclear Information System (INIS)

Zifan, A; Chapman, B E; Liatsis, P

2013-01-01

In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging. (paper)

The use of the Kalman filter in the automated segmentation of EIT lung images.

Science.gov (United States)

Zifan, A; Liatsis, P; Chapman, B E

2013-06-01

In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging.

Full Waveform Inversion Using Nonlinearly Smoothed Wavefields

KAUST Repository

Li, Y.; Choi, Yun Seok; Alkhalifah, Tariq Ali; Li, Z.

2017-01-01

The lack of low frequency information in the acquired data makes full waveform inversion (FWI) conditionally converge to the accurate solution. An initial velocity model that results in data with events within a half cycle of their location in the observed data was required to converge. The multiplication of wavefields with slightly different frequencies generates artificial low frequency components. This can be effectively utilized by multiplying the wavefield with itself, which is nonlinear operation, followed by a smoothing operator to extract the artificially produced low frequency information. We construct the objective function using the nonlinearly smoothed wavefields with a global-correlation norm to properly handle the energy imbalance in the nonlinearly smoothed wavefield. Similar to the multi-scale strategy, we progressively reduce the smoothing width applied to the multiplied wavefield to welcome higher resolution. We calculate the gradient of the objective function using the adjoint-state technique, which is similar to the conventional FWI except for the adjoint source. Examples on the Marmousi 2 model demonstrate the feasibility of the proposed FWI method to mitigate the cycle-skipping problem in the case of a lack of low frequency information.

Full Waveform Inversion Using Nonlinearly Smoothed Wavefields

KAUST Repository

Li, Y.

2017-05-26

The lack of low frequency information in the acquired data makes full waveform inversion (FWI) conditionally converge to the accurate solution. An initial velocity model that results in data with events within a half cycle of their location in the observed data was required to converge. The multiplication of wavefields with slightly different frequencies generates artificial low frequency components. This can be effectively utilized by multiplying the wavefield with itself, which is nonlinear operation, followed by a smoothing operator to extract the artificially produced low frequency information. We construct the objective function using the nonlinearly smoothed wavefields with a global-correlation norm to properly handle the energy imbalance in the nonlinearly smoothed wavefield. Similar to the multi-scale strategy, we progressively reduce the smoothing width applied to the multiplied wavefield to welcome higher resolution. We calculate the gradient of the objective function using the adjoint-state technique, which is similar to the conventional FWI except for the adjoint source. Examples on the Marmousi 2 model demonstrate the feasibility of the proposed FWI method to mitigate the cycle-skipping problem in the case of a lack of low frequency information.

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.

Modeling and control of three phase rectifier with electronic smoothing inductor

DEFF Research Database (Denmark)

Singh, Yash Veer; Rasmussen, Peter Omand; Andersen, Torben Ole

2011-01-01

This paper presents a simple, direct method for deriving the approximate, small-signal, average model and control strategy for three-phase diode bridge rectifier operating with electronic smoothing technique. Electronic smoothing inductor (ESI) performs the function of an inductor that has...... controlled variable impedance. This increases power factor (PF) and reduces total harmonic distortions (THDs) in mains current. The ESI based rectifier enables compact and cost effective design of three phase electric drive as size of passive components is reduced significantly. In order to carry out...

On Predictive Coding for Erasure Channels Using a Kalman Framework

DEFF Research Database (Denmark)

Arildsen, Thomas; Murthi, Manohar; Andersen, Søren Vang

2009-01-01

We present a new design method for robust low-delay coding of autoregressive sources for transmission across erasure channels. It is a fundamental rethinking of existing concepts. It considers the encoder a mechanism that produces signal measurements from which the decoder estimates the original...... signal. The method is based on linear predictive coding and Kalman estimation at the decoder. We employ a novel encoder state-space representation with a linear quantization noise model. The encoder is represented by the Kalman measurement at the decoder. The presented method designs the encoder...... and decoder offline through an iterative algorithm based on closed-form minimization of the trace of the decoder state error covariance. The design method is shown to provide considerable performance gains, when the transmitted quantized prediction errors are subject to loss, in terms of signal-to-noise ratio...

Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter.

Science.gov (United States)

Vafamand, Navid; Arefi, Mohammad Mehdi; Khayatian, Alireza

2018-03-01

This paper proposes two novel Kalman-based learning algorithms for an online Takagi-Sugeno (TS) fuzzy model identification. The proposed approaches are designed based on the unscented Kalman filter (UKF) and the concept of dual estimation. Contrary to the extended Kalman filter (EKF) which utilizes derivatives of nonlinear functions, the UKF employs the unscented transformation. Consequently, non-differentiable membership functions can be considered in the structure of the TS models. This makes the proposed algorithms to be applicable for the online parameter calculation of wider classes of TS models compared to the recently published papers concerning the same issue. Furthermore, because of the great capability of the UKF in handling severe nonlinear dynamics, the proposed approaches can effectively approximate the nonlinear systems. Finally, numerical and practical examples are provided to show the advantages of the proposed approaches. Simulation results reveal the effectiveness of the proposed methods and performance improvement based on the root mean square (RMS) of the estimation error compared to the existing results. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

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.

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.

Incorporation of Time Delayed Measurements in a Discrete-time Kalman Filter

DEFF Research Database (Denmark)

Larsen, Thomas Dall; Andersen, Nils Axel; Ravn, Ole

1998-01-01

In many practical systems there is a delay in some of the sensor devices, for instance vision measurements that may have a long processing time. How to fuse these measurements in a Kalman filter is not a trivial problem if the computational delay is critical. Depending on how much time...... using past and present estimates of the Kalman filter and calculating an optimal gain for this extrapolated measurement...... there is at hand, the designer has to make trade offs between optimality and computational burden of the filter. In this paper various methods in the literature along with a new method proposed by the authors will be presented and compared. The new method is based on “extrapolating” the measurement to present time...

Effects of Piecewise Spatial Smoothing in 4-D SPECT Reconstruction

Science.gov (United States)

Qi, Wenyuan; Yang, Yongyi; King, Michael A.

2014-02-01

further reduce the error level in the myocardium in 4-D reconstruction along with motion-compensated temporal smoothing. In contrast to quadratic spatial smoothing, TV smoothing could reduce the noise level in the LV at a faster pace than the increase in the bias level, thereby achieving a net decrease in the error level. In particular, at the same noise level, TV smoothing could reduce the bias by about 30% compared to quadratic smoothing. Moreover, the CHO results indicate that TV could also improve the lesion detectability even when the lesion is small. The PAC results show that, at the same noise level, TV smoothing achieved lower temporal bias, which is also consistent with the improved spatial resolution of the LV in reconstruction. The improvement in blurring effects by TV was also observed in the clinical images.

Change detection in the dynamics of an intracellular protein synthesis model using nonlinear Kalman filtering.

Science.gov (United States)

Rigatos, Gerasimos G; Rigatou, Efthymia G; Djida, Jean Daniel

2015-10-01

A method for early diagnosis of parametric changes in intracellular protein synthesis models (e.g. the p53 protein - mdm2 inhibitor model) is developed with the use of a nonlinear Kalman Filtering approach (Derivative-free nonlinear Kalman Filter) and of statistical change detection methods. The intracellular protein synthesis dynamic model is described by a set of coupled nonlinear differential equations. It is shown that such a dynamical system satisfies differential flatness properties and this allows to transform it, through a change of variables (diffeomorphism), to the so-called linear canonical form. For the linearized equivalent of the dynamical system, state estimation can be performed using the Kalman Filter recursion. Moreover, by applying an inverse transformation based on the previous diffeomorphism it becomes also possible to obtain estimates of the state variables of the initial nonlinear model. By comparing the output of the Kalman Filter (which is assumed to correspond to the undistorted dynamical model) with measurements obtained from the monitored protein synthesis system, a sequence of differences (residuals) is obtained. The statistical processing of the residuals with the use of x2 change detection tests, can provide indication within specific confidence intervals about parametric changes in the considered biological system and consequently indications about the appearance of specific diseases (e.g. malignancies).

Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method

Institute of Scientific and Technical Information of China (English)

杨海; 李威; 罗成名

2015-01-01

Pure inertial navigation system (INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network (WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter (KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system (FIS), and the fuzzy adaptive Kalman filter (FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.

Smooth polyhedral surfaces

KAUST Repository

Gü nther, Felix; Jiang, Caigui; Pottmann, Helmut

2017-01-01

Polyhedral surfaces are fundamental objects in architectural geometry and industrial design. Whereas closeness of a given mesh to a smooth reference surface and its suitability for numerical simulations were already studied extensively, the aim of our work is to find and to discuss suitable assessments of smoothness of polyhedral surfaces that only take the geometry of the polyhedral surface itself into account. Motivated by analogies to classical differential geometry, we propose a theory of smoothness of polyhedral surfaces including suitable notions of normal vectors, tangent planes, asymptotic directions, and parabolic curves that are invariant under projective transformations. It is remarkable that seemingly mild conditions significantly limit the shapes of faces of a smooth polyhedral surface. Besides being of theoretical interest, we believe that smoothness of polyhedral surfaces is of interest in the architectural context, where vertices and edges of polyhedral surfaces are highly visible.

Smooth polyhedral surfaces

KAUST Repository

Günther, Felix

2017-03-15

Polyhedral surfaces are fundamental objects in architectural geometry and industrial design. Whereas closeness of a given mesh to a smooth reference surface and its suitability for numerical simulations were already studied extensively, the aim of our work is to find and to discuss suitable assessments of smoothness of polyhedral surfaces that only take the geometry of the polyhedral surface itself into account. Motivated by analogies to classical differential geometry, we propose a theory of smoothness of polyhedral surfaces including suitable notions of normal vectors, tangent planes, asymptotic directions, and parabolic curves that are invariant under projective transformations. It is remarkable that seemingly mild conditions significantly limit the shapes of faces of a smooth polyhedral surface. Besides being of theoretical interest, we believe that smoothness of polyhedral surfaces is of interest in the architectural context, where vertices and edges of polyhedral surfaces are highly visible.

Overexpression of Mitofusin 2 inhibited oxidized low-density lipoprotein induced vascular smooth muscle cell proliferation and reduced atherosclerotic lesion formation in rabbit

International Nuclear Information System (INIS)

Guo Yanhong; Chen Kuanghueih; Gao Wei; Li Qian; Chen Li; Wang Guisong; Tang Jian

2007-01-01

Our previous studies have implies that Mitofusin 2 (Mfn2), which was progressively reduced in arteries from ApoE -/- mice during the development of atherosclerosis, may take part in pathogenesis of atherosclerosis. In this study, we found that overexpression of Mfn2 inhibited oxidized low-density lipoprotein or serum induced vascular smooth muscle cell proliferation by down-regulation of Akt and ERK phosphorylation. Then we investigated the in vivo role of Mfn2 on the development of atherosclerosis in rabbits using adenovirus expressing Mitofusin 2 gene (AdMfn2). By morphometric analysis we found overexpression of Mfn2 inhibited atherosclerotic lesion formation and intima/media ratio by 66.7% and 74.6%, respectively, compared with control group. These results suggest that local Mfn2 treatment suppresses the development of atherosclerosis in vivo in part by attenuating the smooth muscle cell proliferation induced by lipid deposition and vascular injury

Algoritma Filter Kalman untuk Menghaluskan Data Pengukuran

Directory of Open Access Journals (Sweden)

Rudiyanto

2006-12-01

Full Text Available The objective of this paper is to apply a simple algorithm of Kalman Filter, wich is know as noise data filtering. The computer program was written in Macro Visual Basic in MS Exel. Testings were carried out on available temperature, Water level and force data and then were comared with the mooving average method. The result shows that the algorithm performed better and lesser deviation than the mooving average.

In Situ Monitoring of Groundwater Contamination Using the Kalman Filter For Sustainable Remediation

Science.gov (United States)

Schmidt, F.; Wainwright, H. M.; Faybishenko, B.; Denham, M. E.; Eddy-Dilek, C. A.

2017-12-01

Sustainable remediation - based on less intensive passive remediation and natural attenuation - has become a desirable remediation alternative at contaminated sites. Although it has a number of benefits, such as reduced waste and water/energy usage, it carries a significant burden of proof to verify plume stability and to ensure insignificant increase of risk to public health. Modeling of contaminant transport is still challenging despite recent advances in numerical methods. Long-term monitoring has, therefore, become a critical component in sustainable remediation. However, the current approach, which relies on sparse groundwater sampling, is problematic, since it could miss sudden significant changes in plume behavior. A new method is needed to combine existing knowledge about contaminant behavior and latest advances in in situ groundwater sensors. This study presents an example of the effective use of the Kalman filter approach to estimate contaminant concentrations, based on in situ measured water quality parameters (e.g. electrical conductivity and pH) along with the results of sparse groundwater sampling. The Kalman filter can effectively couple physical models and data correlations between the contaminant concentrations and in situ measured variables. We aim (1) to develop a framework capable of integrating different data types to provide accurate contaminant concentration estimates, (2) to demonstrate that these results remain reliable, even when the groundwater sampling frequency is reduced, and (3) to evaluate the future efficacy of this strategy using reactive transport simulations. This framework can also serve as an early warning system for detecting unexpected plume migration. We demonstrate our approach using historical and current groundwater data from the Savannah River Site (SRS) F-Area Seepage Basins to estimate uranium and tritium concentrations. The results show that the developed method can provide reliable estimates of contaminant

Earth orientation parameters from VLBI determined with a Kalman filter

Directory of Open Access Journals (Sweden)

Maria Karbon

2017-11-01

We prove that the Kalman filter is more than on par with the classical least squares method and that it is a valuable alternative, especially on the advent of the VLBI2010 Global Observing System and within the GGOS frame work.

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

SmoothMoves : Smooth pursuits head movements for augmented reality

NARCIS (Netherlands)

Esteves, Augusto; Verweij, David; Suraiya, Liza; Islam, Rasel; Lee, Youryang; Oakley, Ian

2017-01-01

SmoothMoves is an interaction technique for augmented reality (AR) based on smooth pursuits head movements. It works by computing correlations between the movements of on-screen targets and the user's head while tracking those targets. The paper presents three studies. The first suggests that head

Application of the extended Kalman filtering for the estimation of core coolant flow rate in pressurized water reactors

International Nuclear Information System (INIS)

Shieh, D.J.; Upadhyaya, B.R.

1986-01-01

In-core neutron detector and core-exit temperature signals in a pressurized water reactor (PWR) satisfy the condition of observability of the core dynamic system, and can be used to estimate nonmeasurable state variables and model parameters. The extension of the Kalman filtering technique is very useful for direct parameter estimation. This approach is applied to the determination of core coolant mass flow rate in PWRs and is evaluated using in-core measurements at the Loss-of-Fluid Test (LOFT) reactor. The influence of model uncertainties on the estimation accuracy was studied using the ambiguity function analysis. A sequential discretization method was developed to achieve faster convergence to the true value, avoiding model discretization at each sample point. The performance of the extended Kalman filter and the computational innovations were evaluated using a reduced order core dynamic model of the LOFT reactor and random data simulation. The technique was then applied to the determination of LOFT core coolant flow rate from operational data at 100% and 65% flow conditions

Application of unscented Kalman filter for condition monitoring of an organic Rankine cycle turbogenerator

DEFF Research Database (Denmark)

Pierobon, Leonardo; Schlanbusch, Rune; Kandepu, Rambabu

2014-01-01

for this project. Considering the plant dynamics, it is of paramount importance to monitor the peak temperatures within the once-through boiler serving the bottoming unit to prevent the decomposition of the working fluid. This paper accordingly aims at applying the unscented Kalman filter to estimate...... the temperature distribution inside the primary heat exchanger by engaging a detailed and distributed model of the system and available measurements. Simulation results prove the robustness of the unscented Kalman filter with respect to process noise, measurement disturbances and initial conditions....

Decentralized identification of nonlinear structure under strong ground motion using the extended Kalman filter and unscented Kalman filter

Science.gov (United States)

Tao, Dongwang; Li, Hui; Ma, Qiang

2016-04-01

Complete structure identification of complicate nonlinear system using extend Kalman filter (EKF) or unscented Kalman filter (UKF) may have the problems of divergence, huge computation and low estimation precision due to the large dimension of the extended state space for the system. In this article, a decentralized identification method of hysteretic system based on the joint EKF and UKF is proposed. The complete structure is divided into linear substructures and nonlinear substructures. The substructures are identified from the top to the bottom. For the linear substructure, EKF is used to identify the extended space including the displacements, velocities, stiffness and damping coefficients of the substructures, using the limited absolute accelerations and the identified interface force above the substructure. Similarly, for the nonlinear substructure, UKF is used to identify the extended space including the displacements, velocities, stiffness, damping coefficients and control parameters for the hysteretic Bouc-Wen model and the force at the interface of substructures. Finally a 10-story shear-type structure with multiple inter-story hysteresis is used for numerical simulation and is identified using the decentralized approach, and the identified results are compared with those using only EKF or UKF for the complete structure identification. The results show that the decentralized approach has the advantage of more stability, relative less computation and higher estimation precision.

Demodulation of moire fringes in digital holographic interferometry using an extended Kalman filter.

Science.gov (United States)

Ramaiah, Jagadesh; Rastogi, Pramod; Rajshekhar, Gannavarpu

2018-03-10

This paper presents a method for extracting multiple phases from a single moire fringe pattern in digital holographic interferometry. The method relies on component separation using singular value decomposition and an extended Kalman filter for demodulating the moire fringes. The Kalman filter is applied by modeling the interference field locally as a multi-component polynomial phase signal and extracting the associated multiple polynomial coefficients using the state space approach. In addition to phase, the corresponding multiple phase derivatives can be simultaneously extracted using the proposed method. The applicability of the proposed method is demonstrated using simulation and experimental results.

Smoothing a Piecewise-Smooth: An Example from Plankton Population Dynamics

DEFF Research Database (Denmark)

Piltz, Sofia Helena

2016-01-01

In this work we discuss a piecewise-smooth dynamical system inspired by plankton observations and constructed for one predator switching its diet between two different types of prey. We then discuss two smooth formulations of the piecewise-smooth model obtained by using a hyperbolic tangent funct...... function and adding a dimension to the system. We compare model behaviour of the three systems and show an example case where the steepness of the switch is determined from a comparison with data on freshwater plankton....

The Ensemble Kalman Filter for Groundwater Plume Characterization: A Case Study.

Science.gov (United States)

Ross, James L; Andersen, Peter F

2018-04-17

The Kalman filter is an efficient data assimilation tool to refine an estimate of a state variable using measured data and the variable's correlations in space and/or time. The ensemble Kalman filter (EnKF) (Evensen, 2004, 2009) is a Kalman filter variant that employs Monte Carlo analysis to define the correlations that help to refine the updated state. While use of EnKF in hydrology is somewhat limited, it has been successfully applied in other fields of engineering (e.g. oil reservoir modeling, weather forecasting). Here, EnKF is used to refine a simulated groundwater TCE plume that underlies the Tooele Army Depot-North (TEAD-N) in Utah, based on observations of TCE in the aquifer. The resulting EnKF-based assimilated plume is simulated forward in time to predict future plume migration. The correlations that underpin EnKF updating implicitly contain information about how the plume developed over time under the influence of complex site hydrology and variable source history, as they are predicated on multiple realizations of a well-calibrated numerical groundwater flow and transport model. The EnKF methodology is compared to an ordinary kriging-based assimilation method with respect to the accurate representation of plume concentrations in order to determine the relative efficacy of EnKF for water quality data assimilation. This article is protected by copyright. All rights reserved.

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.

An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine Interfaces.

Science.gov (United States)

Li, Simin; Li, Jie; Li, Zheng

2016-01-01

Brain-machine interfaces (BMIs) seek to connect brains with machines or computers directly, for application in areas such as prosthesis control. For this application, the accuracy of the decoding of movement intentions is crucial. We aim to improve accuracy by designing a better encoding model of primary motor cortical activity during hand movements and combining this with decoder engineering refinements, resulting in a new unscented Kalman filter based decoder, UKF2, which improves upon our previous unscented Kalman filter decoder, UKF1. The new encoding model includes novel acceleration magnitude, position-velocity interaction, and target-cursor-distance features (the decoder does not require target position as input, it is decoded). We add a novel probabilistic velocity threshold to better determine the user's intent to move. We combine these improvements with several other refinements suggested by others in the field. Data from two Rhesus monkeys indicate that the UKF2 generates offline reconstructions of hand movements (mean CC 0.851) significantly more accurately than the UKF1 (0.833) and the popular position-velocity Kalman filter (0.812). The encoding model of the UKF2 could predict the instantaneous firing rate of neurons (mean CC 0.210), given kinematic variables and past spiking, better than the encoding models of these two decoders (UKF1: 0.138, p-v Kalman: 0.098). In closed-loop experiments where each monkey controlled a computer cursor with each decoder in turn, the UKF2 facilitated faster task completion (mean 1.56 s vs. 2.05 s) and higher Fitts's Law bit rate (mean 0.738 bit/s vs. 0.584 bit/s) than the UKF1. These results suggest that the modeling and decoder engineering refinements of the UKF2 improve decoding performance. We believe they can be used to enhance other decoders as well.

Comparison of optical beam smoothing techniques for inertial confinement fusion and improvement of smoothing by the use of zero-correlation masks

International Nuclear Information System (INIS)

Lehmberg, R. H.; Rothenberg, J. E.

2000-01-01

We present analytic theory and numerical simulations comparing the optical beam smoothing capabilities of the smoothing by spectral dispersion (SSD) technique using random temporal phase modulation, with that of the induced spatial incoherence technique. The analytic theory provides a simple formula for the SSD mode spectrum in the usual case where the phase mask at the focusing lens is random, and its asymptotic limit quantitatively relates the long wavelength mode smoothing to the width of the angular dispersion. With parameters and phase aberration relevant to the National Ignition Facility beams, the SSD simulations show that the large long wavelength components, which are also found in earlier simulations, can be significantly reduced by replacing the independent random phase masks in each pair of adjacent beams by a conjugate pair of zero-correlation masks. These simulations suggest that one can combine zero-correlation masks with random temporal phase modulation and multiple color cycles to achieve SSD smoothing approaching the optical bandwidth limit at all spatial frequencies, without using large angular dispersions. (c) 2000 American Institute of Physics

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.

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.

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.

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

Energy Technology Data Exchange (ETDEWEB)

Cerati, Giuseppe [Fermilab; Elmer, Peter [Princeton U.; Krutelyov, Slava [UC, San Diego; Lantz, Steven [Cornell U.; Lefebvre, Matthieu [Princeton U.; Masciovecchio, Mario [UC, San Diego; McDermott, Kevin [Cornell U.; Riley, Daniel [Cornell U., LNS; Tadel, Matevž [UC, San Diego; Wittich, Peter [Cornell U.; Würthwein, Frank [UC, San Diego; Yagil, Avi [UC, San Diego

2017-01-01

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.

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

RSSI-Based Distance Estimation Framework Using a Kalman Filter for Sustainable Indoor Computing Environments

Directory of Open Access Journals (Sweden)

Yunsick Sung

2016-11-01

Full Text Available Given that location information is the key to providing a variety of services in sustainable indoor computing environments, it is required to obtain accurate locations. Locations can be estimated by three distances from three fixed points. Therefore, if the distance between two points can be measured or estimated accurately, the location in indoor environments can be estimated. To increase the accuracy of the measured distance, noise filtering, signal revision, and distance estimation processes are generally performed. This paper proposes a novel framework for estimating the distance between a beacon and an access point (AP in a sustainable indoor computing environment. Diverse types of received strength signal indications (RSSIs are used for WiFi, Bluetooth, and radio signals, and the proposed distance estimation framework is unique in that it is independent of the specific wireless signal involved, being based on the Bluetooth signal of the beacon. Generally, RSSI measurement, noise filtering, and revision are required for distance estimation using RSSIs. The employed RSSIs are first measured from an AP, with multiple APs sometimes used to increase the accuracy of the distance estimation. Owing to the inevitable presence of noise in the measured RSSIs, the application of noise filtering is essential, and further revision is used to address the inaccuracy and instability that characterizes RSSIs measured in an indoor environment. The revised RSSIs are then used to estimate the distance. The proposed distance estimation framework uses one AP to measure the RSSIs, a Kalman filter to eliminate noise, and a log-distance path loss model to revise the measured RSSIs. In the experimental implementation of the framework, both a RSSI filter and a Kalman filter were respectively used for noise elimination to comparatively evaluate the performance of the latter for the specific application. The Kalman filter was found to reduce the accumulated errors by 8

Kalman filtered MR temperature imaging for laser induced thermal therapies.

Science.gov (United States)

Fuentes, D; Yung, J; Hazle, J D; Weinberg, J S; Stafford, R J

2012-04-01

The feasibility of using a stochastic form of Pennes bioheat model within a 3-D finite element based Kalman filter (KF) algorithm is critically evaluated for the ability to provide temperature field estimates in the event of magnetic resonance temperature imaging (MRTI) data loss during laser induced thermal therapy (LITT). The ability to recover missing MRTI data was analyzed by systematically removing spatiotemporal information from a clinical MR-guided LITT procedure in human brain and comparing predictions in these regions to the original measurements. Performance was quantitatively evaluated in terms of a dimensionless L(2) (RMS) norm of the temperature error weighted by acquisition uncertainty. During periods of no data corruption, observed error histories demonstrate that the Kalman algorithm does not alter the high quality temperature measurement provided by MR thermal imaging. The KF-MRTI implementation considered is seen to predict the bioheat transfer with RMS error 10 sec.

An example of utilization of Kalman filters in time series analysis

International Nuclear Information System (INIS)

Marseguerra, M.; Porceddu, C.M.

1987-01-01

In reactor noise analysis the fluctuation of many interesting signals may be described by linear models such as the AR, ARMA or ARMAX ones. Another interesting approach of increasing importance is the Kalman filter methodology. In the this paper a linear system described by an autoregressive AR(2) model is considered and it is investigated whether the Kalman filter is capable of correctly estimating parameters together with their accuracies both in the stationary state and in the case of sudden variation of the parameters. In addition a more complex situation in which a stationary system under investigation feeds the sensor which delivers the observed signal. Assuming the system obeying on AR(2) model and the sensor a simpler AR(1) model, the problem is that of recovering the system output from the measured signal

Discrete integration of continuous Kalman filtering equations for time invariant second-order structural systems

Science.gov (United States)

Park, K. C.; Belvin, W. Keith

1990-01-01

A general form for the first-order representation of the continuous second-order linear structural-dynamics equations is introduced to derive a corresponding form of first-order continuous Kalman filtering equations. Time integration of the resulting equations is carried out via a set of linear multistep integration formulas. It is shown that a judicious combined selection of computational paths and the undetermined matrices introduced in the general form of the first-order linear structural systems leads to a class of second-order discrete Kalman filtering equations involving only symmetric sparse N x N solution matrices.

Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures

Energy Technology Data Exchange (ETDEWEB)

Cerati, Giuseppe [Fermilab; Elmer, Peter [Princeton U.; Krutelyov, Slava [UC, San Diego; Lantz, Steven [Cornell U., Phys. Dept.; Lefebvre, Matthieu [Princeton U.; Masciovecchio, Mario [UC, San Diego; McDermott, Kevin [Cornell U., Phys. Dept.; Riley, Daniel [Cornell U., Phys. Dept.; Tadel, Matevž [UC, San Diego; Wittich, Peter [Cornell U., Phys. Dept.; Würthwein, Frank [UC, San Diego; Yagil, Avi [UC, San Diego

2017-11-16

Faced with physical and energy density limitations on clock speed, contemporary microprocessor designers have increasingly turned to on-chip parallelism for performance gains. Examples include the Intel Xeon Phi, GPGPUs, and similar technologies. Algorithms should accordingly be designed with ample amounts of fine-grained parallelism if they are to realize the full performance of the hardware. This requirement can be challenging for algorithms that are naturally expressed as a sequence of small-matrix operations, such as the Kalman filter methods widely in use in high-energy physics experiments. In the High-Luminosity Large Hadron Collider (HL-LHC), for example, one of the dominant computational problems is expected to be finding and fitting charged-particle tracks during event reconstruction; today, the most common track-finding methods are those based on the Kalman filter. Experience at the LHC, both in the trigger and offline, has shown that these methods are robust and provide high physics performance. Previously we reported the significant parallel speedups that resulted from our efforts to adapt Kalman-filter-based tracking to many-core architectures such as Intel Xeon Phi. Here we report on how effectively those techniques can be applied to more realistic detector configurations and event complexity.

A Bioinspired Neural Model Based Extended Kalman Filter for Robot SLAM

Directory of Open Access Journals (Sweden)

Jianjun Ni

2014-01-01

Full Text Available Robot simultaneous localization and mapping (SLAM problem is a very important and challenging issue in the robotic field. The main tasks of SLAM include how to reduce the localization error and the estimated error of the landmarks and improve the robustness and accuracy of the algorithms. The extended Kalman filter (EKF based method is one of the most popular methods for SLAM. However, the accuracy of the EKF based SLAM algorithm will be reduced when the noise model is inaccurate. To solve this problem, a novel bioinspired neural model based SLAM approach is proposed in this paper. In the proposed approach, an adaptive EKF based SLAM structure is proposed, and a bioinspired neural model is used to adjust the weights of system noise and observation noise adaptively, which can guarantee the stability of the filter and the accuracy of the SLAM algorithm. The proposed approach can deal with the SLAM problem in various situations, for example, the noise is in abnormal conditions. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach.

Hybrid Levenberg–Marquardt and Weak-Constraint Ensemble Kalman Smoother Method

Czech Academy of Sciences Publication Activity Database

Mandel, Jan; Bergou, E.; Gürol, S.; Gratton, S.; Kasanický, Ivan

2016-01-01

Roč. 23, č. 2 (2016), s. 59-73 ISSN 1023-5809 R&D Projects: GA ČR GA13-34856S Institutional support: RVO:67985807 Keywords : data assimilation * numerical methods * Kalman filter Subject RIV: IN - Informatics, Computer Science Impact factor: 1.329, year: 2016

A Novel Orally Available Asthma Drug Candidate That Reduces Smooth Muscle Constriction and Inflammation by Targeting GABAA Receptors in the Lung.

Science.gov (United States)

Forkuo, Gloria S; Nieman, Amanda N; Kodali, Revathi; Zahn, Nicolas M; Li, Guanguan; Rashid Roni, M S; Stephen, Michael Rajesh; Harris, Ted W; Jahan, Rajwana; Guthrie, Margaret L; Yu, Olivia B; Fisher, Janet L; Yocum, Gene T; Emala, Charles W; Steeber, Douglas A; Stafford, Douglas C; Cook, James M; Arnold, Leggy A

2018-05-07

We describe lead compound MIDD0301 for the oral treatment of asthma based on previously developed positive allosteric α 5 β 3 γ 2 selective GABA A receptor (GABA A R) ligands. MIDD0301 relaxed airway smooth muscle at single micromolar concentrations as demonstrated with ex vivo guinea pig tracheal rings. MIDD0301 also attenuated airway hyperresponsiveness (AHR) in an ovalbumin murine model of asthma by oral administration. Reduced numbers of eosinophils and macrophages were observed in mouse bronchoalveolar lavage fluid without changing mucous metaplasia. Importantly, lung cytokine expression of IL-17A, IL-4, and TNF-α were reduced for MIDD0301-treated mice without changing antiinflammatory cytokine IL-10 levels. Automated patch clamp confirmed amplification of GABA induced current mediated by α 1-3,5 β 3 γ 2 GABA A Rs in the presence of MIDD0301. Pharmacodynamically, transmembrane currents of ex vivo CD4 + T cells from asthmatic mice were potentiated by MIDD0301 in the presence of GABA. The number of CD4 + T cells observed in the lung of MIDD0301-treated mice were reduced by an oral treatment of 20 mg/kg b.i.d. for 5 days. A half-life of almost 14 h was demonstrated by pharmacokinetic studies (PK) with no adverse CNS effects when treated mice were subjected to sensorimotor studies using the rotarod. PK studies also confirmed very low brain distribution. In conclusion, MIDD0301 represents a safe and improved oral asthma drug candidate that relaxes airway smooth muscle and attenuates inflammation in the lung leading to a reduction of AHR at a dosage lower than earlier reported GABA A R ligands.

A Damping Grid Strapdown Inertial Navigation System Based on a Kalman Filter for Ships in Polar Regions.

Science.gov (United States)

Huang, Weiquan; Fang, Tao; Luo, Li; Zhao, Lin; Che, Fengzhu

2017-07-03

The grid strapdown inertial navigation system (SINS) used in polar navigation also includes three kinds of periodic oscillation errors as common SINS are based on a geographic coordinate system. Aiming ships which have the external information to conduct a system reset regularly, suppressing the Schuler periodic oscillation is an effective way to enhance navigation accuracy. The Kalman filter based on the grid SINS error model which applies to the ship is established in this paper. The errors of grid-level attitude angles can be accurately estimated when the external velocity contains constant error, and then correcting the errors of the grid-level attitude angles through feedback correction can effectively dampen the Schuler periodic oscillation. The simulation results show that with the aid of external reference velocity, the proposed external level damping algorithm based on the Kalman filter can suppress the Schuler periodic oscillation effectively. Compared with the traditional external level damping algorithm based on the damping network, the algorithm proposed in this paper can reduce the overshoot errors when the state of grid SINS is switched from the non-damping state to the damping state, and this effectively improves the navigation accuracy of the system.

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.

Schmidt-Kalman Filter with Polynomial Chaos Expansion for Orbit Determination of Space Objects

Science.gov (United States)

Yang, Y.; Cai, H.; Zhang, K.

2016-09-01

Parameter errors in orbital models can result in poor orbit determination (OD) using a traditional Kalman filter. One approach to account for these errors is to consider them in the so-called Schmidt-Kalman filter (SKF), by augmenting the state covariance matrix (CM) with additional parameter covariance rather than additively estimating these so-called "consider" parameters. This paper introduces a new SKF algorithm with polynomial chaos expansion (PCE-SKF). The PCE approach has been proved to be more efficient than Monte Carlo method for propagating the input uncertainties onto the system response without experiencing any constraints of linear dynamics, or Gaussian distributions of the uncertainty sources. The state and covariance needed in the orbit prediction step are propagated using PCE. An inclined geosynchronous orbit scenario is set up to test the proposed PCE-SKF based OD algorithm. The satellite orbit is propagated based on numerical integration, with the uncertain coefficient of solar radiation pressure considered. The PCE-SKF solutions are compared with extended Kalman filter (EKF), SKF and PCE-EKF (EKF with PCE) solutions. It is implied that the covariance propagation using PCE leads to more precise OD solutions in comparison with those based on linear propagation of covariance.

SKENARIO TENGGANG WAKTU SST NINO 3.4 TERHADAP CURAH HUJAN UNTUK MENINGKATKAN AKURASI PREDIKSI KALMAN FILTER

Directory of Open Access Journals (Sweden)

Restu Tresnawati

2014-06-01

Full Text Available Prediksi curah hujan bulanan menggunakan prediktor SST (Sea Surface Temperature Nino 3.4 harus diketahui apakah secara langsung dalam waktu bersamaan mempengaruhi curah hujan. Pada penelitian ini, skenario tenggang waktu (time lag diujicobakan untuk meningkatkan akurasi prediksi curah hujan bulanan dengan Kalman Filter. Pada tahap pertama, SST Nino 3.4 pada lag 0, lag 1, lag 2 diprediksi menggunakan ARIMA. Kemudian hasil ini digunakan sebagai salah satu prediktor dalam Kalman Filter. Penelitian diujicobakan terhadap validasi prediksi curah hujan bulanan di daerah Purbalingga selama periode tiga tahun kebelakang (hindcast 2006, 2007, 2008. Dari hasil penelitian diperoleh bahwa tenggang waktu diperlukan dalam prediksi.   Monthly rainfall forecasting using Sea Surface Temperature (SST Nino 3.4 as predictor must be known of how directly effect on rainfall. In this paper, time lag scenarios are proposed for increase prediction accurately of Kalman Filter. First, SST Nino 3.4 on lag 0, lag 1, and lag 2 are predicted by ARIMA. Then, this result is used as one of predictor in the Kalman Filter Prediction. This method is attempted for validation of monthly rainfall forecasting in Purbalingga by three-year period (2006,2007,2009 of hind cast. Experimental results show that time lag are needed in Monthly rainfall forecasting.

Speed Estimation of Induction Motor Using Model Reference Adaptive System with Kalman Filter

Directory of Open Access Journals (Sweden)

Pavel Brandstetter

2013-01-01

Full Text Available The paper deals with a speed estimation of the induction motor using observer with Model Reference Adaptive System and Kalman Filter. For simulation, Hardware in Loop Simulation method is used. The first part of the paper includes the mathematical description of the observer for the speed estimation of the induction motor. The second part describes Kalman filter. The third part describes Hardware in Loop Simulation method and its realization using multifunction card MF 624. In the last section of the paper, simulation results are shown for different changes of the induction motor speed which confirm high dynamic properties of the induction motor drive with sensorless control.

Vinpocetine Attenuates the Osteoblastic Differentiation of Vascular Smooth Muscle Cells.

Directory of Open Access Journals (Sweden)

Yun-Yun Ma

Full Text Available Vascular calcification is an active process of osteoblastic differentiation of vascular smooth muscle cells; however, its definite mechanism remains unknown. Vinpocetine, a derivative of the alkaloid vincamine, has been demonstrated to inhibit the high glucose-induced proliferation of vascular smooth muscle cells; however, it remains unknown whether vinpocetine can affect the osteoblastic differentiation of vascular smooth muscle cells. We hereby investigated the effect of vinpocetine on vascular calcification using a beta-glycerophosphate-induced cell model. Our results showed that vinpocetine significantly reduced the osteoblast-like phenotypes of vascular smooth muscle cells including ALP activity, osteocalcin, collagen type I, Runx2 and BMP-2 expression as well as the formation of mineralized nodule. Vinpocetine, binding to translocation protein, induced phosphorylation of extracellular signal-related kinase and Akt and thus inhibited the translocation of nuclear factor-kappa B into the nucleus. Silencing of translocator protein significantly attenuated the inhibitory effect of vinpocetine on osteoblastic differentiation of vascular smooth muscle cells. Taken together, vinpocetine may be a promising candidate for the clinical therapy of vascular calcification.

Non-linear DSGE Models and The Central Difference Kalman Filter

DEFF Research Database (Denmark)

Andreasen, Martin Møller

This paper introduces a Quasi Maximum Likelihood (QML) approach based on the Cen- tral Difference Kalman Filter (CDKF) to estimate non-linear DSGE models with potentially non-Gaussian shocks. We argue that this estimator can be expected to be consistent and asymptotically normal for DSGE models...

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...

A Method for SINS Alignment with Large Initial Misalignment Angles Based on Kalman Filter with Parameters Resetting

Directory of Open Access Journals (Sweden)

Xixiang Liu

2014-01-01

Full Text Available In the initial alignment process of strapdown inertial navigation system (SINS, large initial misalignment angles always bring nonlinear problem, which causes alignment failure when the classical linear error model and standard Kalman filter are used. In this paper, the problem of large misalignment angles in SINS initial alignment is investigated, and the key reason for alignment failure is given as the state covariance from Kalman filter cannot represent the true one during the steady filtering process. According to the analysis, an alignment method for SINS based on multiresetting the state covariance matrix of Kalman filter is designed to deal with large initial misalignment angles, in which classical linear error model and standard Kalman filter are used, but the state covariance matrix should be multireset before the steady process until large misalignment angles are decreased to small ones. The performance of the proposed method is evaluated by simulation and car test, and the results indicate that the proposed method can fulfill initial alignment with large misalignment angles effectively and the alignment accuracy of the proposed method is as precise as that of alignment with small misalignment angles.

A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty

International Nuclear Information System (INIS)

Li, Yanwen; Wang, Chao; Gong, Jinfeng

2016-01-01

An accurate battery State of Charge estimation plays an important role in battery electric vehicles. This paper makes two contributions to the existing literature. (1) A recursive least squares method with fuzzy adaptive forgetting factor has been presented to update the model parameters close to the real value more quickly. (2) The statistical information of the innovation sequence obeying chi-square distribution has been introduced to identify model uncertainty, and a novel combination algorithm of strong tracking unscented Kalman filter and adaptive unscented Kalman filter has been developed to estimate SOC (State of Charge). Experimental results indicate that the novel algorithm has a good performance in estimating the battery SOC against initial SOC errors and voltage sensor drift. A comparison with the unscented Kalman filter-based algorithms and adaptive unscented Kalman filter-based algorithms shows that the proposed SOC estimation method has better accuracy, robustness and convergence behavior. - Highlights: • Recursive least squares method with fuzzy adaptive forgetting factor is presented. • The innovation obeying chi-square distribution is used to identify uncertainty. • A combination Karman filter approach for State of Charge estimation is presented. • The performance of the proposed method is verified by comparison results.

Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction

Science.gov (United States)

Li, Zhencai; Wang, Yang; Liu, Zhen

2016-01-01

The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703

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.

Evaluation of an image-based tracking workflow with Kalman filtering for automatic image plane alignment in interventional MRI.

Science.gov (United States)

Neumann, M; Cuvillon, L; Breton, E; de Matheli, M

2013-01-01

Recently, a workflow for magnetic resonance (MR) image plane alignment based on tracking in real-time MR images was introduced. The workflow is based on a tracking device composed of 2 resonant micro-coils and a passive marker, and allows for tracking of the passive marker in clinical real-time images and automatic (re-)initialization using the microcoils. As the Kalman filter has proven its benefit as an estimator and predictor, it is well suited for use in tracking applications. In this paper, a Kalman filter is integrated in the previously developed workflow in order to predict position and orientation of the tracking device. Measurement noise covariances of the Kalman filter are dynamically changed in order to take into account that, according to the image plane orientation, only a subset of the 3D pose components is available. The improved tracking performance of the Kalman extended workflow could be quantified in simulation results. Also, a first experiment in the MRI scanner was performed but without quantitative results yet.

Kinematic Localization for Global Navigation Satellite Systems: A Kalman Filtering Approach

Science.gov (United States)

Tabatabaee, Mohammad Hadi

Use of the Global Positioning System (GNSS) has expanded significantly in the past decade, especially with advances in embedded systems and the emergence of smartphones and the Internet of Things (IoT). The growing demand has stimulated research on development of GNSS techniques and programming tools. The focus of much of the research efforts have been on high-level algorithms and augmentations. This dissertation focuses on the low-level methods at the heart of GNSS systems and proposes a new methods for GNSS positioning problems based on concepts of distance geometry and the use of Kalman filters. The methods presented in this dissertation provide algebraic solutions to problems that have predominantly been solved using iterative methods. The proposed methods are highly efficient, provide accurate estimates, and exhibit a degree of robustness in the presence of unfavorable satellite geometry. The algorithm operates in two stages; an estimation of the receiver clock bias and removal of the bias from the pseudorange observables, followed by the localization of the GNSS receiver. The use of a Kalman filter in between the two stages allows for an improvement of the clock bias estimate with a noticeable impact on the position estimates. The receiver localization step has also been formulated in a linear manner allowing for the direct application of a Kalman filter without any need for linearization. The methodology has also been extended to double differential observables for high accuracy pseudorange and carrier phase position estimates.

Comparison of different incremental analysis update schemes in a realistic assimilation system with Ensemble Kalman Filter

Science.gov (United States)

Yan, Y.; Barth, A.; Beckers, J. M.; Brankart, J. M.; Brasseur, P.; Candille, G.

2017-07-01

In this paper, three incremental analysis update schemes (IAU 0, IAU 50 and IAU 100) are compared in the same assimilation experiments with a realistic eddy permitting primitive equation model of the North Atlantic Ocean using the Ensemble Kalman Filter. The difference between the three IAU schemes lies on the position of the increment update window. The relevance of each IAU scheme is evaluated through analyses on both thermohaline and dynamical variables. The validation of the assimilation results is performed according to both deterministic and probabilistic metrics against different sources of observations. For deterministic validation, the ensemble mean and the ensemble spread are compared to the observations. For probabilistic validation, the continuous ranked probability score (CRPS) is used to evaluate the ensemble forecast system according to reliability and resolution. The reliability is further decomposed into bias and dispersion by the reduced centred random variable (RCRV) score. The obtained results show that 1) the IAU 50 scheme has the same performance as the IAU 100 scheme 2) the IAU 50/100 schemes outperform the IAU 0 scheme in error covariance propagation for thermohaline variables in relatively stable region, while the IAU 0 scheme outperforms the IAU 50/100 schemes in dynamical variables estimation in dynamically active region 3) in case with sufficient number of observations and good error specification, the impact of IAU schemes is negligible. The differences between the IAU 0 scheme and the IAU 50/100 schemes are mainly due to different model integration time and different instability (density inversion, large vertical velocity, etc.) induced by the increment update. The longer model integration time with the IAU 50/100 schemes, especially the free model integration, on one hand, allows for better re-establishment of the equilibrium model state, on the other hand, smooths the strong gradients in dynamically active region.

A Novel Kalman Filter for Human Motion Tracking With an Inertial-Based Dynamic Inclinometer.

Science.gov (United States)

Ligorio, Gabriele; Sabatini, Angelo M

2015-08-01

Design and development of a linear Kalman filter to create an inertial-based inclinometer targeted to dynamic conditions of motion. The estimation of the body attitude (i.e., the inclination with respect to the vertical) was treated as a source separation problem to discriminate the gravity and the body acceleration from the specific force measured by a triaxial accelerometer. The sensor fusion between triaxial gyroscope and triaxial accelerometer data was performed using a linear Kalman filter. Wrist-worn inertial measurement unit data from ten participants were acquired while performing two dynamic tasks: 60-s sequence of seven manual activities and 90 s of walking at natural speed. Stereophotogrammetric data were used as a reference. A statistical analysis was performed to assess the significance of the accuracy improvement over state-of-the-art approaches. The proposed method achieved, on an average, a root mean square attitude error of 3.6° and 1.8° in manual activities and locomotion tasks (respectively). The statistical analysis showed that, when compared to few competing methods, the proposed method improved the attitude estimation accuracy. A novel Kalman filter for inertial-based attitude estimation was presented in this study. A significant accuracy improvement was achieved over state-of-the-art approaches, due to a filter design that better matched the basic optimality assumptions of Kalman filtering. Human motion tracking is the main application field of the proposed method. Accurately discriminating the two components present in the triaxial accelerometer signal is well suited for studying both the rotational and the linear body kinematics.

Using Kalman Filter to Guarantee QoS Robustness of Web Server

Institute of Scientific and Technical Information of China (English)

无

2006-01-01

The exponential growth of the Internet coupled with the increasing popularity of dynamically generated content on the World Wide Web, has created the need for more and faster Web servers capable of serving the over 100 million Internet users. To converge the control method has emerged as a promising technique to solve the Web QoS problem. In this paper, a model of adaptive session is presented and a session flow self-regulating algorism based on Kalman Filter are proposed towards Web Server. And a Web QoS self-regulating scheme is advanced. To attain the goal of on-line system identification, the optimized estimation of QoS parameters is fulfilled by utilizing Kalman Filter in full domain. The simulation results shows that the proposed scheme can guarantee the QoS with both robustness and stability .

Pedestrian Tracking Based on Camshift with Kalman Prediction for Autonomous Vehicles

Directory of Open Access Journals (Sweden)

Lie Guo

2016-06-01

Full Text Available Pedestrian detection and tracking is the key to autonomous vehicle navigation systems avoiding potentially dangerous situations. Firstly, the probability distribution of colour information is established after a pedestrian is located in an image. Then the detected results are utilized to initialize a Kalman filter to predict the possible position of the pedestrian centroid in the future frame. A Camshift tracking algorithm is used to track the pedestrian in the specific search window of the next frame based on the prediction results. The actual position of the pedestrian centroid is output from the Camshift tracking algorithm to update the gain and error covariance matrix of the Kalman filter. Experimental results in real traffic situations show the proposed pedestrian tracking algorithm can achieve good performance even when they are partly occluded in inconsistent illumination circumstances.

Distributed multisensor data fusion based on Kalman filtering and the parallel implementation%基于Kalman滤波的多传感器分布式数据融合算法及其并行实现

Institute of Scientific and Technical Information of China (English)

郭强; 郁松年

2006-01-01

The purpose of data fusion is to produce an improved model or estimate of a system from a set of independent data sources. Various multisensor data fusion approaches exist, in which Kalman filtering is important. In this paper, a fusion algorithm based on multisensor systems is discussed and a distributed multisensor data fusion algorithm based on Kalman filtering presented. The algorithm has been implemented on cluster-based high performance computers. Experimental results show that the method produces precise estimation in considerably reduced execution time.

Real-time MR diffusion tensor and Q-ball imaging using Kalman filtering

International Nuclear Information System (INIS)

Poupon, C.; Roche, A.; Dubois, J.; Mangin, J.F.; Poupon, F.

2008-01-01

Diffusion magnetic resonance imaging (dMRI) has become an established research tool for the investigation of tissue structure and orientation. In this paper, we present a method for real-time processing of diffusion tensor and Q-ball imaging. The basic idea is to use Kalman filtering framework to fit either the linear tensor or Q-ball model. Because the Kalman filter is designed to be an incremental algorithm, it naturally enables updating the model estimate after the acquisition of any new diffusion-weighted volume. Processing diffusion models and maps during ongoing scans provides a new useful tool for clinicians, especially when it is not possible to predict how long a subject may remain still in the magnet. First, we introduce the general linear models corresponding to the two diffusion tensor and analytical Q-ball models of interest. Then, we present the Kalman filtering framework and we focus on the optimization of the diffusion orientation sets in order to speed up the convergence of the online processing. Last, we give some results on a healthy volunteer for the online tensor and the Q-ball model, and we make some comparisons with the conventional offline techniques used in the literature. We could achieve full real-time for diffusion tensor imaging and deferred time for Q-ball imaging, using a single workstation. (authors)

A User Guide for Smoothing Air Traffic Radar Data

Science.gov (United States)

Bach, Ralph E.; Paielli, Russell A.

2014-01-01

Matlab software was written to provide smoothing of radar tracking data to simulate ADS-B (Automatic Dependent Surveillance-Broadcast) data in order to test a tactical conflict probe. The probe, called TSAFE (Tactical Separation-Assured Flight Environment), is designed to handle air-traffic conflicts left undetected or unresolved when loss-of-separation is predicted to occur within approximately two minutes. The data stream that is down-linked from an aircraft equipped with an ADS-B system would include accurate GPS-derived position and velocity information at sample rates of 1 Hz. Nation-wide ADS-B equipage (mandated by 2020) should improve surveillance accuracy and TSAFE performance. Currently, position data are provided by Center radar (nominal 12-sec samples) and Terminal radar (nominal 4.8-sec samples). Aircraft ground speed and ground track are estimated using real-time filtering, causing lags up to 60 sec, compromising performance of a tactical resolution tool. Offline smoothing of radar data reduces wild-point errors, provides a sample rate as high as 1 Hz, and yields more accurate and lag-free estimates of ground speed, ground track, and climb rate. Until full ADS-B implementation is available, smoothed radar data should provide reasonable track estimates for testing TSAFE in an ADS-B-like environment. An example illustrates the smoothing of radar data and shows a comparison of smoothed-radar and ADS-B tracking. This document is intended to serve as a guide for using the smoothing software.

Efficient data assimilation algorithm for bathymetry application

Science.gov (United States)

Ghorbanidehno, H.; Lee, J. H.; Farthing, M.; Hesser, T.; Kitanidis, P. K.; Darve, E. F.

2017-12-01

Information on the evolving state of the nearshore zone bathymetry is crucial to shoreline management, recreational safety, and naval operations. The high cost and complex logistics of using ship-based surveys for bathymetry estimation have encouraged the use of remote sensing techniques. Data assimilation methods combine the remote sensing data and nearshore hydrodynamic models to estimate the unknown bathymetry and the corresponding uncertainties. In particular, several recent efforts have combined Kalman Filter-based techniques such as ensembled-based Kalman filters with indirect video-based observations to address the bathymetry inversion problem. However, these methods often suffer from ensemble collapse and uncertainty underestimation. Here, the Compressed State Kalman Filter (CSKF) method is used to estimate the bathymetry based on observed wave celerity. In order to demonstrate the accuracy and robustness of the CSKF method, we consider twin tests with synthetic observations of wave celerity, while the bathymetry profiles are chosen based on surveys taken by the U.S. Army Corps of Engineer Field Research Facility (FRF) in Duck, NC. The first test case is a bathymetry estimation problem for a spatially smooth and temporally constant bathymetry profile. The second test case is a bathymetry estimation problem for a temporally evolving bathymetry from a smooth to a non-smooth profile. For both problems, we compare the results of CSKF with those obtained by the local ensemble transform Kalman filter (LETKF), which is a popular ensemble-based Kalman filter method.

Detection of Sensor Faults in Small Helicopter UAVs Using Observer/Kalman Filter Identification

Directory of Open Access Journals (Sweden)

Guillermo Heredia

2011-01-01

Full Text Available Reliability is a critical issue in navigation of unmanned aerial vehicles (UAVs since there is no human pilot that can react to any abnormal situation. Due to size and cost limitations, redundant sensor schemes and aeronautical-grade navigation sensors used in large aircrafts cannot be installed in small UAVs. Therefore, other approaches like analytical redundancy should be used to detect faults in navigation sensors and increase reliability. This paper presents a sensor fault detection and diagnosis system for small autonomous helicopters based on analytical redundancy. Fault detection is accomplished by evaluating any significant change in the behaviour of the vehicle with respect to the fault-free behaviour, which is estimated by using an observer. The observer is obtained from input-output experimental data with the Observer/Kalman Filter Identification (OKID method. The OKID method is able to identify the system and an observer with properties similar to a Kalman filter, directly from input-output experimental data. Results are similar to the Kalman filter, but, with the proposed method, there is no need to estimate neither system matrices nor sensor and process noise covariance matrices. The system has been tested with real helicopter flight data, and the results compared with other methods.

A large-scale application of the Kalman alignment algorithm to the CMS tracker

International Nuclear Information System (INIS)

Widl, E; Fruehwirth, R

2008-01-01

The Kalman alignment algorithm has been specifically developed to cope with the demands that arise from the specifications of the CMS Tracker. The algorithmic concept is based on the Kalman filter formalism and is designed to avoid the inversion of large matrices. Most notably, the algorithm strikes a balance between conventional global and local track-based alignment algorithms, by restricting the computation of alignment parameters not only to alignable objects hit by the same track, but also to all other alignable objects that are significantly correlated. Nevertheless, this feature also comes with various trade-offs: Mechanisms are needed that affect which alignable objects are significantly correlated and keep track of these correlations. Due to the large amount of alignable objects involved at each update (at least compared to local alignment algorithms), the time spent for retrieving and writing alignment parameters as well as the required user memory becomes a significant factor. The large-scale test presented here applies the Kalman alignment algorithm to the (misaligned) CMS Tracker barrel, and demonstrates the feasibility of the algorithm in a realistic scenario. It is shown that both the computation time and the amount of required user memory are within reasonable bounds, given the available computing resources, and that the obtained results are satisfactory

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.

Adaptive quantization-parameter clip scheme for smooth quality in H.264/AVC.

Science.gov (United States)

Hu, Sudeng; Wang, Hanli; Kwong, Sam

2012-04-01

In this paper, we investigate the issues over the smooth quality and the smooth bit rate during rate control (RC) in H.264/AVC. An adaptive quantization-parameter (Q(p)) clip scheme is proposed to optimize the quality smoothness while keeping the bit-rate fluctuation at an acceptable level. First, the frame complexity variation is studied by defining a complexity ratio between two nearby frames. Second, the range of the generated bits is analyzed to prevent the encoder buffer from overflow and underflow. Third, based on the safe range of the generated bits, an optimal Q(p) clip range is developed to reduce the quality fluctuation. Experimental results demonstrate that the proposed Q(p) clip scheme can achieve excellent performance in quality smoothness and buffer regulation.

Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

KAUST Repository

Altaf, Muhammad

2013-08-01

This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H∞ filter. By design, an H∞ filter is more robust than the common Kalman filter in the sense that the estimation error in the H∞ filter has, in general, a finite growth rate with respect to the uncertainties in assimilation. The computational hydrodynamical model used in this study is the Advanced Circulation (ADCIRC) model. The authors assimilate data obtained from Hurricanes Katrina and Ike as test cases. The results clearly show that the H∞-based SEIK filter provides more accurate short-range forecasts of storm surge compared to recently reported data assimilation results resulting from the standard SEIK filter.

Improving Short-Range Ensemble Kalman Storm Surge Forecasting Using Robust Adaptive Inflation

KAUST Repository

Altaf, Muhammad; Butler, T.; Luo, X.; Dawson, C.; Mayo, T.; Hoteit, Ibrahim

2013-01-01

This paper presents a robust ensemble filtering methodology for storm surge forecasting based on the singular evolutive interpolated Kalman (SEIK) filter, which has been implemented in the framework of the H∞ filter. By design, an H∞ filter is more robust than the common Kalman filter in the sense that the estimation error in the H∞ filter has, in general, a finite growth rate with respect to the uncertainties in assimilation. The computational hydrodynamical model used in this study is the Advanced Circulation (ADCIRC) model. The authors assimilate data obtained from Hurricanes Katrina and Ike as test cases. The results clearly show that the H∞-based SEIK filter provides more accurate short-range forecasts of storm surge compared to recently reported data assimilation results resulting from the standard SEIK filter.

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.

Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach

International Nuclear Information System (INIS)

Chen, Kuilin; Yu, Jie

2014-01-01

Highlights: • A novel hybrid modeling method is proposed for short-term wind speed forecasting. • Support vector regression model is constructed to formulate nonlinear state-space framework. • Unscented Kalman filter is adopted to recursively update states under random uncertainty. • The new SVR–UKF approach is compared to several conventional methods for short-term wind speed prediction. • The proposed method demonstrates higher prediction accuracy and reliability. - Abstract: Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations

Classification of smooth Fano polytopes

DEFF Research Database (Denmark)

Øbro, Mikkel

A simplicial lattice polytope containing the origin in the interior is called a smooth Fano polytope, if the vertices of every facet is a basis of the lattice. The study of smooth Fano polytopes is motivated by their connection to toric varieties. The thesis concerns the classification of smooth...... Fano polytopes up to isomorphism. A smooth Fano -polytope can have at most vertices. In case of vertices an explicit classification is known. The thesis contains the classification in case of vertices. Classifications of smooth Fano -polytopes for fixed exist only for . In the thesis an algorithm...... for the classification of smooth Fano -polytopes for any given is presented. The algorithm has been implemented and used to obtain the complete classification for ....

Estimation of aerosol particle number distribution with Kalman Filtering – Part 2: Simultaneous use of DMPS, APS and nephelometer measurements

Directory of Open Access Journals (Sweden)

T. Viskari

2012-12-01

Full Text Available Extended Kalman Filter (EKF is used to estimate particle size distributions from observations. The focus here is on the practical application of EKF to simultaneously merge information from different types of experimental instruments. Every 10 min, the prior state estimate is updated with size-segregating measurements from Differential Mobility Particle Sizer (DMPS and Aerodynamic Particle Sizer (APS as well as integrating measurements from a nephelometer. Error covariances are approximate in our EKF implementation. The observation operator assumes a constant particle density and refractive index. The state estimates are compared to particle size distributions that are a composite of DMPS and APS measurements. The impact of each instrument on the size distribution estimate is studied. Kalman Filtering of DMPS and APS yielded a temporally consistent state estimate. This state estimate is continuous over the overlapping size range of DMPS and APS. Inclusion of the integrating measurements further reduces the effect of measurement noise. Even with the present approximations, EKF is shown to be a very promising method to estimate particle size distribution with observations from different types of instruments.

Adaptive Kalman filtering for real-time mapping of the visual field

Science.gov (United States)

Ward, B. Douglas; Janik, John; Mazaheri, Yousef; Ma, Yan; DeYoe, Edgar A.

2013-01-01

This paper demonstrates the feasibility of real-time mapping of the visual field for clinical applications. Specifically, three aspects of this problem were considered: (1) experimental design, (2) statistical analysis, and (3) display of results. Proper experimental design is essential to achieving a successful outcome, particularly for real-time applications. A random-block experimental design was shown to have less sensitivity to measurement noise, as well as greater robustness to error in modeling of the hemodynamic impulse response function (IRF) and greater flexibility than common alternatives. In addition, random encoding of the visual field allows for the detection of voxels that are responsive to multiple, not necessarily contiguous, regions of the visual field. Due to its recursive nature, the Kalman filter is ideally suited for real-time statistical analysis of visual field mapping data. An important feature of the Kalman filter is that it can be used for nonstationary time series analysis. The capability of the Kalman filter to adapt, in real time, to abrupt changes in the baseline arising from subject motion inside the scanner and other external system disturbances is important for the success of clinical applications. The clinician needs real-time information to evaluate the success or failure of the imaging run and to decide whether to extend, modify, or terminate the run. Accordingly, the analytical software provides real-time displays of (1) brain activation maps for each stimulus segment, (2) voxel-wise spatial tuning profiles, (3) time plots of the variability of response parameters, and (4) time plots of activated volume. PMID:22100663

Exponential smoothing weighted correlations

Science.gov (United States)

Pozzi, F.; Di Matteo, T.; Aste, T.

2012-06-01

In many practical applications, correlation matrices might be affected by the "curse of dimensionality" and by an excessive sensitiveness to outliers and remote observations. These shortcomings can cause problems of statistical robustness especially accentuated when a system of dynamic correlations over a running window is concerned. These drawbacks can be partially mitigated by assigning a structure of weights to observational events. In this paper, we discuss Pearson's ρ and Kendall's τ correlation matrices, weighted with an exponential smoothing, computed on moving windows using a data-set of daily returns for 300 NYSE highly capitalized companies in the period between 2001 and 2003. Criteria for jointly determining optimal weights together with the optimal length of the running window are proposed. We find that the exponential smoothing can provide more robust and reliable dynamic measures and we discuss that a careful choice of the parameters can reduce the autocorrelation of dynamic correlations whilst keeping significance and robustness of the measure. Weighted correlations are found to be smoother and recovering faster from market turbulence than their unweighted counterparts, helping also to discriminate more effectively genuine from spurious correlations.

A Matrix-Free Posterior Ensemble Kalman Filter Implementation Based on a Modified Cholesky Decomposition

Directory of Open Access Journals (Sweden)

Elias D. Nino-Ruiz

2017-07-01

Full Text Available In this paper, a matrix-free posterior ensemble Kalman filter implementation based on a modified Cholesky decomposition is proposed. The method works as follows: the precision matrix of the background error distribution is estimated based on a modified Cholesky decomposition. The resulting estimator can be expressed in terms of Cholesky factors which can be updated based on a series of rank-one matrices in order to approximate the precision matrix of the analysis distribution. By using this matrix, the posterior ensemble can be built by either sampling from the posterior distribution or using synthetic observations. Furthermore, the computational effort of the proposed method is linear with regard to the model dimension and the number of observed components from the model domain. Experimental tests are performed making use of the Lorenz-96 model. The results reveal that, the accuracy of the proposed implementation in terms of root-mean-square-error is similar, and in some cases better, to that of a well-known ensemble Kalman filter (EnKF implementation: the local ensemble transform Kalman filter. In addition, the results are comparable to those obtained by the EnKF with large ensemble sizes.

Kalman filter tracking on parallel architectures

Science.gov (United States)

Cerati, G.; Elmer, P.; Krutelyov, S.; Lantz, S.; Lefebvre, M.; McDermott, K.; Riley, D.; Tadel, M.; Wittich, P.; Wurthwein, F.; Yagil, A.

2017-10-01

We report on the progress of our studies towards a Kalman filter track reconstruction algorithm with optimal performance on manycore architectures. The combinatorial structure of these algorithms is not immediately compatible with an efficient SIMD (or SIMT) implementation; the challenge for us is to recast the existing software so it can readily generate hundreds of shared-memory threads that exploit the underlying instruction set of modern processors. We show how the data and associated tasks can be organized in a way that is conducive to both multithreading and vectorization. We demonstrate very good performance on Intel Xeon and Xeon Phi architectures, as well as promising first results on Nvidia GPUs.

Effective Five Directional Partial Derivatives-Based Image Smoothing and a Parallel Structure Design.

Science.gov (United States)

Choongsang Cho; Sangkeun Lee

2016-04-01

Image smoothing has been used for image segmentation, image reconstruction, object classification, and 3D content generation. Several smoothing approaches have been used at the pre-processing step to retain the critical edge, while removing noise and small details. However, they have limited performance, especially in removing small details and smoothing discrete regions. Therefore, to provide fast and accurate smoothing, we propose an effective scheme that uses a weighted combination of the gradient, Laplacian, and diagonal derivatives of a smoothed image. In addition, to reduce computational complexity, we designed and implemented a parallel processing structure for the proposed scheme on a graphics processing unit (GPU). For an objective evaluation of the smoothing performance, the images were linearly quantized into several layers to generate experimental images, and the quantized images were smoothed using several methods for reconstructing the smoothly changed shape and intensity of the original image. Experimental results showed that the proposed scheme has higher objective scores and better successful smoothing performance than similar schemes, while preserving and removing critical and trivial details, respectively. For computational complexity, the proposed smoothing scheme running on a GPU provided 18 and 16 times lower complexity than the proposed smoothing scheme running on a CPU and the L0-based smoothing scheme, respectively. In addition, a simple noise reduction test was conducted to show the characteristics of the proposed approach; it reported that the presented algorithm outperforms the state-of-the art algorithms by more than 5.4 dB. Therefore, we believe that the proposed scheme can be a useful tool for efficient image smoothing.

Cholesterol is necessary both for the toxic effect of Abeta peptides on vascular smooth muscle cells and for Abeta binding to vascular smooth muscle cell membranes.

Science.gov (United States)

Subasinghe, Supundi; Unabia, Sharon; Barrow, Colin J; Mok, Su San; Aguilar, Marie-Isabel; Small, David H

2003-02-01

Accumulation of beta amyloid (Abeta) in the brain is central to the pathogenesis of Alzheimer's disease. Abeta can bind to membrane lipids and this binding may have detrimental effects on cell function. In this study, surface plasmon resonance technology was used to study Abeta binding to membranes. Abeta peptides bound to synthetic lipid mixtures and to an intact plasma membrane preparation isolated from vascular smooth muscle cells. Abeta peptides were also toxic to vascular smooth muscle cells. There was a good correlation between the toxic effect of Abeta peptides and their membrane binding. 'Ageing' the Abeta peptides by incubation for 5 days increased the proportion of oligomeric species, and also increased toxicity and the amount of binding to lipids. The toxicities of various Abeta analogs correlated with their lipid binding. Significantly, binding was influenced by the concentration of cholesterol in the lipid mixture. Reduction of cholesterol in vascular smooth muscle cells not only reduced the binding of Abeta to purified plasma membrane preparations but also reduced Abeta toxicity. The results support the view that Abeta toxicity is a direct consequence of binding to lipids in the membrane. Reduction of membrane cholesterol using cholesterol-lowering drugs may be of therapeutic benefit because it reduces Abeta-membrane binding.

Advanced Kalman Filter for Real-Time Responsiveness in Complex Systems

Energy Technology Data Exchange (ETDEWEB)

Welch, Gregory Francis [UNC-Chapel Hill/University of Central Florida; Zhang, Jinghe [UNC-Chapel Hill/Virginia Tech

2014-06-10

Complex engineering systems pose fundamental challenges in real-time operations and control because they are highly dynamic systems consisting of a large number of elements with severe nonlinearities and discontinuities. Today’s tools for real-time complex system operations are mostly based on steady state models, unable to capture the dynamic nature and too slow to prevent system failures. We developed advanced Kalman filtering techniques and the formulation of dynamic state estimation using Kalman filtering techniques to capture complex system dynamics in aiding real-time operations and control. In this work, we looked at complex system issues including severe nonlinearity of system equations, discontinuities caused by system controls and network switches, sparse measurements in space and time, and real-time requirements of power grid operations. We sought to bridge the disciplinary boundaries between Computer Science and Power Systems Engineering, by introducing methods that leverage both existing and new techniques. While our methods were developed in the context of electrical power systems, they should generalize to other large-scale scientific and engineering applications.

A Kalman Filter for SINS Self-Alignment Based on Vector Observation.

Science.gov (United States)

Xu, Xiang; Xu, Xiaosu; Zhang, Tao; Li, Yao; Tong, Jinwu

2017-01-29

In this paper, a self-alignment method for strapdown inertial navigation systems based on the q -method is studied. In addition, an improved method based on integrating gravitational apparent motion to form apparent velocity is designed, which can reduce the random noises of the observation vectors. For further analysis, a novel self-alignment method using a Kalman filter based on adaptive filter technology is proposed, which transforms the self-alignment procedure into an attitude estimation using the observation vectors. In the proposed method, a linear psuedo-measurement equation is adopted by employing the transfer method between the quaternion and the observation vectors. Analysis and simulation indicate that the accuracy of the self-alignment is improved. Meanwhile, to improve the convergence rate of the proposed method, a new method based on parameter recognition and a reconstruction algorithm for apparent gravitation is devised, which can reduce the influence of the random noises of the observation vectors. Simulations and turntable tests are carried out, and the results indicate that the proposed method can acquire sound alignment results with lower standard variances, and can obtain higher alignment accuracy and a faster convergence rate.

Signal Tracking Beyond the Time Resolution of an Atomic Sensor by Kalman Filtering

Science.gov (United States)

Jiménez-Martínez, Ricardo; Kołodyński, Jan; Troullinou, Charikleia; Lucivero, Vito Giovanni; Kong, Jia; Mitchell, Morgan W.

2018-01-01

We study causal waveform estimation (tracking) of time-varying signals in a paradigmatic atomic sensor, an alkali vapor monitored by Faraday rotation probing. We use Kalman filtering, which optimally tracks known linear Gaussian stochastic processes, to estimate stochastic input signals that we generate by optical pumping. Comparing the known input to the estimates, we confirm the accuracy of the atomic statistical model and the reliability of the Kalman filter, allowing recovery of waveform details far briefer than the sensor's intrinsic time resolution. With proper filter choice, we obtain similar benefits when tracking partially known and non-Gaussian signal processes, as are found in most practical sensing applications. The method evades the trade-off between sensitivity and time resolution in coherent sensing.

Processing Functional Near Infrared Spectroscopy Signal with a Kalman Filter to Assess Working Memory during Simulated Flight.

Science.gov (United States)

Durantin, Gautier; Scannella, Sébastien; Gateau, Thibault; Delorme, Arnaud; Dehais, Frédéric

2015-01-01

Working memory (WM) is a key executive function for operating aircraft, especially when pilots have to recall series of air traffic control instructions. There is a need to implement tools to monitor WM as its limitation may jeopardize flight safety. An innovative way to address this issue is to adopt a Neuroergonomics approach that merges knowledge and methods from Human Factors, System Engineering, and Neuroscience. A challenge of great importance for Neuroergonomics is to implement efficient brain imaging techniques to measure the brain at work and to design Brain Computer Interfaces (BCI). We used functional near infrared spectroscopy as it has been already successfully tested to measure WM capacity in complex environment with air traffic controllers (ATC), pilots, or unmanned vehicle operators. However, the extraction of relevant features from the raw signal in ecological environment is still a critical issue due to the complexity of implementing real-time signal processing techniques without a priori knowledge. We proposed to implement the Kalman filtering approach, a signal processing technique that is efficient when the dynamics of the signal can be modeled. We based our approach on the Boynton model of hemodynamic response. We conducted a first experiment with nine participants involving a basic WM task to estimate the noise covariances of the Kalman filter. We then conducted a more ecological experiment in our flight simulator with 18 pilots who interacted with ATC instructions (two levels of difficulty). The data was processed with the same Kalman filter settings implemented in the first experiment. This filter was benchmarked with a classical pass-band IIR filter and a Moving Average Convergence Divergence (MACD) filter. Statistical analysis revealed that the Kalman filter was the most efficient to separate the two levels of load, by increasing the observed effect size in prefrontal areas involved in WM. In addition, the use of a Kalman filter increased

Processing Functional Near Infrared Spectroscopy Signal with a Kalman Filter to Assess Working Memory during Simulated Flight.

Directory of Open Access Journals (Sweden)

Gautier eDurantin

2016-01-01

Full Text Available Working memory is a key executive function for operating aircraft, especially when pilots have to recall series of air traffic control instructions. There is a need to implement tools to monitor working memory as its limitation may jeopardize flight safety. An innovative way to address this issue is to adopt a Neuroergonomics approach that merges knowledge and methods from Human Factors, System Engineering and Neuroscience. A challenge of great importance for Neuroergonomics is to implement efficient brain imaging techniques to measure the brain at work and to design Brain Computer Interfaces. We used functional near infrared spectroscopy as it has been already successfully tested to measure working memory capacity in complex environment with air traffic controllers, pilots or unmanned vehicle operators. However, the extraction of relevant features from the raw signal in ecological environment is still a critical issue due to the complexity of implementing real-time signal processing techniques without a priori knowledge. We proposed to implement the Kalman filtering approach, a signal processing technique that is efficient when the dynamics of the signal can be modeled. We based our approach on the Boynton model of hemodynamic response. We conducted a first experiment with 9 participants involving a basic working memory task to estimate the noise covariances of the Kalman filter. We then conducted a more ecological experiment in our flight simulator with 18 pilots who interacted with air traffic controller instructions (two levels of difficulty. The data was processed with the same Kalman filter settings implemented in the first experiment. This filter was benchmarked with a classical pass-band IIR filter and a Moving Average Convergence Divergence filter. Statistical analysis revealed that the Kalman filter was the most efficient to separate the two levels of load, by increasing the observed effect size in prefrontal areas involved in working

Soft sensing for two-phase flow using an ensemble Kalman filter

NARCIS (Netherlands)

Gryzlov, A.; Leskens, M.; Mudde, R.F.

2009-01-01

A new approach for real-time monitoring of horizontal wells, which is based on data assimilation concepts, is presented. Such methodology can be used when the direct measurement of multiphase flow rates is unfeasible or even unavailable. The real-time estimator proposed is an ensemble Kalman filter

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.

Real time eye tracking using Kalman extended spatio-temporal context learning

Science.gov (United States)

Munir, Farzeen; Minhas, Fayyaz ul Amir Asfar; Jalil, Abdul; Jeon, Moongu

2017-06-01

Real time eye tracking has numerous applications in human computer interaction such as a mouse cursor control in a computer system. It is useful for persons with muscular or motion impairments. However, tracking the movement of the eye is complicated by occlusion due to blinking, head movement, screen glare, rapid eye movements, etc. In this work, we present the algorithmic and construction details of a real time eye tracking system. Our proposed system is an extension of Spatio-Temporal context learning through Kalman Filtering. Spatio-Temporal Context Learning offers state of the art accuracy in general object tracking but its performance suffers due to object occlusion. Addition of the Kalman filter allows the proposed method to model the dynamics of the motion of the eye and provide robust eye tracking in cases of occlusion. We demonstrate the effectiveness of this tracking technique by controlling the computer cursor in real time by eye movements.

卫星轨道Kalman滤波稳健估计%obust Kalman Filtering for Satellite Orbit Determination

Institute of Scientific and Technical Information of China (English)

文援兰; 王威; 杨元喜

2001-01-01

Kalman filtering is affected by the gross error that is inevitable in the observation of satellite. First robust kalman filtering is derived and its robustness is analyzed, then the observations of Lageos is processed. It verifies that robust kalman filtering has the capability to resist the gross error.%卫星观测数据中不可避免地存在着粗差，一般的Kalman滤波易受观测粗差的影响。首先推导Kalman滤波稳健估计公式，并分析了它的稳健性。然后用Kalman滤波稳健估计对Lageos卫星的激光实测资料进行了处理，证明它具有明显的抗粗差的能力和稳健性。

Inexpensive CubeSat attitude estimation using COTS components and Unscented Kalman Filtering

DEFF Research Database (Denmark)

Larsen, Jesper Abildgaard; Vinther, Kasper

2011-01-01

computational cost of estimating bias in measurements is worthwhile. The simulations where performed in a simulation environment for the CubeSat AAUSAT3, where robustness has been an important factor during tuning of the attitude estimators. The results indicate that it is possible to achieve acceptable Cube......This paper describes a quaternion implementation of an Unscented Kalman Filter for attitude estimation on CubeSats using measurements of a sun vector, a magnetic field vector and angular velocity. Using unit quaternions provides a singularity free attitude parameterization. However, the unity...... constraint requires a redesign of the Unscented Kalman Filter. Therefore, a quaternion error state is introduced. Emphasis has been put in making the implementation accessible to other CubeSat by using realistic models of COTS components used for attitude sensing and simulations have shown that the extra...

Kalman-filter model for determining block and trickle SNM losses

International Nuclear Information System (INIS)

Barlow, R.E.; Durst, M.J.; Smiriga, N.G.

1982-07-01

This paper describes an integrated decision procedure for deciding whether a diversion of SNM has occurred. Two possible types of diversion are considered: a block loss during a single time period and a cumulative trickle loss over several time periods. The methodology used is based on a compound Kalman filter model. Numerical examples illustrate our approach

Human lung mast cells modulate the functions of airway smooth muscle cells in asthma.

Science.gov (United States)

Alkhouri, H; Hollins, F; Moir, L M; Brightling, C E; Armour, C L; Hughes, J M

2011-09-01

Activated mast cell densities are increased on the airway smooth muscle in asthma where they may modulate muscle functions and thus contribute to airway inflammation, remodelling and airflow obstruction. To determine the effects of human lung mast cells on the secretory and proliferative functions of airway smooth muscle cells from donors with and without asthma. Freshly isolated human lung mast cells were stimulated with IgE/anti-IgE. Culture supernatants were collected after 2 and 24 h and the mast cells lysed. The supernatants/lysates were added to serum-deprived, subconfluent airway smooth muscle cells for up to 48 h. Released chemokines and extracellular matrix were measured by ELISA, proliferation was quantified by [(3) H]-thymidine incorporation and cell counting, and intracellular signalling by phospho-arrays. Mast cell 2-h supernatants reduced CCL11 and increased CXCL8 and fibronectin production from both asthmatic and nonasthmatic muscle cells. Leupeptin reversed these effects. Mast cell 24-h supernatants and lysates reduced CCL11 release from both muscle cell types but increased CXCL8 release by nonasthmatic cells. The 24-h supernatants also reduced asthmatic, but not nonasthmatic, muscle cell DNA synthesis and asthmatic cell numbers over 5 days through inhibiting extracellular signal-regulated kinase (ERK) and phosphatidylinositol (PI3)-kinase pathways. However, prostaglandins, thromboxanes, IL-4 and IL-13 were not involved in reducing the proliferation. Mast cell proteases and newly synthesized products differentially modulated the secretory and proliferative functions of airway smooth muscle cells from donors with and without asthma. Thus, mast cells may modulate their own recruitment and airway smooth muscle functions locally in asthma. © 2011 John Wiley & Sons A/S.

Smoothing dynamic positron emission tomography time courses using functional principal components

OpenAIRE

Jiang, Ci-Ren; Aston, John A. D.; Wang, Jane-Ling

2009-01-01

A functional smoothing approach to the analysis of PET time course data is presented. By borrowing information across space and accounting for this pooling through the use of a non-parametric covariate adjustment, it is possible to smooth the PET time course data thus reducing the noise. A new model for functional data analysis, the Multiplicative Nonparametric Random Effects Model, is introduced to more accurately account for the variation in the data. A locally adaptive bandwidth choice hel...

Analysis of Dynamic Performance of a Kalman Filter for Combining Multiple MEMS Gyroscopes

Directory of Open Access Journals (Sweden)

Liang Xue

2014-11-01

Full Text Available In this paper, the dynamic performance of a Kalman filter (KF was analyzed, which is used to combine multiple measurements of a gyroscopes array to reduce the noise and improve the accuracy of the individual sensors. A principle for accuracy improvement by the KF was briefly presented to obtain an optimal estimate of input rate signal. In particular, the influences of some crucial factors on the KF dynamic performance were analyzed by simulations such as the factors input signal frequency, signal sampling, and KF filtering rate. Finally, a system that was comprised of a six-gyroscope array was designed and implemented to test the dynamic performance. Experimental results indicated that the 1σ error for the combined rate signal was reduced to about 0.2°/s in the constant rate test, which was a reduction by a factor of more than eight compared to the single gyroscope. The 1σ error was also reduced from 1.6°/s to 0.48°/s in the swing test. It showed that the estimated angular rate signal could well reflect the dynamic characteristic of the input signal in dynamic conditions.

Kalman filters for assimilating near-surface observations into the Richards equation - Part 2: A dual filter approach for simultaneous retrieval of states and parameters

Science.gov (United States)

Medina, H.; Romano, N.; Chirico, G. B.

2014-07-01

This study presents a dual Kalman filter (DSUKF - dual standard-unscented Kalman filter) for retrieving states and parameters controlling the soil water dynamics in a homogeneous soil column, by assimilating near-surface state observations. The DSUKF couples a standard Kalman filter for retrieving the states of a linear solver of the Richards equation, and an unscented Kalman filter for retrieving the parameters of the soil hydraulic functions, which are defined according to the van Genuchten-Mualem closed-form model. The accuracy and the computational expense of the DSUKF are compared with those of the dual ensemble Kalman filter (DEnKF) implemented with a nonlinear solver of the Richards equation. Both the DSUKF and the DEnKF are applied with two alternative state-space formulations of the Richards equation, respectively differentiated by the type of variable employed for representing the states: either the soil water content (θ) or the soil water matric pressure head (h). The comparison analyses are conducted with reference to synthetic time series of the true states, noise corrupted observations, and synthetic time series of the meteorological forcing. The performance of the retrieval algorithms are examined accounting for the effects exerted on the output by the input parameters, the observation depth and assimilation frequency, as well as by the relationship between retrieved states and assimilated variables. The uncertainty of the states retrieved with DSUKF is considerably reduced, for any initial wrong parameterization, with similar accuracy but less computational effort than the DEnKF, when this is implemented with ensembles of 25 members. For ensemble sizes of the same order of those involved in the DSUKF, the DEnKF fails to provide reliable posterior estimates of states and parameters. The retrieval performance of the soil hydraulic parameters is strongly affected by several factors, such as the initial guess of the unknown parameters, the wet or dry

The second order extended Kalman filter and Markov nonlinear filter for data processing in interferometric systems

International Nuclear Information System (INIS)

Ermolaev, P; Volynsky, M

2014-01-01

Recurrent stochastic data processing algorithms using representation of interferometric signal as output of a dynamic system, which state is described by vector of parameters, in some cases are more effective, compared with conventional algorithms. Interferometric signals depend on phase nonlinearly. Consequently it is expedient to apply algorithms of nonlinear stochastic filtering, such as Kalman type filters. An application of the second order extended Kalman filter and Markov nonlinear filter that allows to minimize estimation error is described. Experimental results of signals processing are illustrated. Comparison of the algorithms is presented and discussed.

Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor

Directory of Open Access Journals (Sweden)

Biçer Cenker

2016-01-01

Full Text Available In this paper, the stability of the adaptive fading extended Kalman filter with the matrix forgetting factor when applied to the state estimation problem with noise terms in the non–linear discrete–time stochastic systems has been analysed. The analysis is conducted in a similar manner to the standard extended Kalman filter’s stability analysis based on stochastic framework. The theoretical results show that under certain conditions on the initial estimation error and the noise terms, the estimation error remains bounded and the state estimation is stable.

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...

Estimation of aircraft aerodynamic derivatives using Extended Kalman Filter

OpenAIRE

Curvo, M.

2000-01-01

Design of flight control laws, verification of performance predictions, and the implementation of flight simulations are tasks that require a mathematical model of the aircraft dynamics. The dynamical models are characterized by coefficients (aerodynamic derivatives) whose values must be determined from flight tests. This work outlines the use of the Extended Kalman Filter (EKF) in obtaining the aerodynamic derivatives of an aircraft. The EKF shows several advantages over the more traditional...

Superimposed chirped pulse parameter estimation based on the extended Kalman filter (EKF)

CSIR Research Space (South Africa)

Olivier, JC

2009-05-01

Full Text Available An extended Kalman filter (EKF) is proposed to estimate the frequencies and chirp rate of multiple superimposed chirped pulses. The estimation problem is a difficult one, where maximum likelyhood methods are very complex especially if more than two...

Does responsive pricing smooth demand shocks?

OpenAIRE

Pascal, Courty; Mario, Pagliero

2011-01-01

Using data from a unique pricing experiment, we investigate Vickrey’s conjecture that responsive pricing can be used to smooth both predictable and unpredictable demand shocks. Our evidence shows that increasing the responsiveness of price to demand conditions reduces the magnitude of deviations in capacity utilization rates from a pre-determined target level. A 10 percent increase in price variability leads to a decrease in the variability of capacity utilization rates between...

Simultaneous Estimation of Model State Variables and Observation and Forecast Biases Using a Two-Stage Hybrid Kalman Filter

Science.gov (United States)

Pauwels, V. R. N.; DeLannoy, G. J. M.; Hendricks Franssen, H.-J.; Vereecken, H.

2013-01-01

In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter

Directory of Open Access Journals (Sweden)

V. R. N. Pauwels

2013-09-01

Full Text Available In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

Bayesian Exponential Smoothing.

OpenAIRE

Forbes, C.S.; Snyder, R.D.; Shami, R.S.

2000-01-01

In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. The approach is based on a state space model containing only a single source of error for each time interval. This model allows us to improve current practices surrounding exponential smoothing by providing both point predictions and measures of the uncertainty surrounding them.

Sky-Hook Control and Kalman Filtering in Nonlinear Model of Tracked Vehicle Suspension System

Directory of Open Access Journals (Sweden)

Jurkiewicz Andrzej

2017-09-01

Full Text Available The essence of the undertaken topic is application of the continuous sky-hook control strategy and the Extended Kalman Filter as the state observer in the 2S1 tracked vehicle suspension system. The half-car model of this suspension system consists of seven logarithmic spiral springs and two magnetorheological dampers which has been described by the Bingham model. The applied continuous sky-hook control strategy considers nonlinear stiffness characteristic of the logarithmic spiral springs. The control is determined on estimates generated by the Extended Kalman Filter. Improve of ride comfort is verified by comparing simulation results, under the same driving conditions, of controlled and passive vehicle suspension systems.

Two-dimensional restoration of single photon emission computed tomography images using the Kalman filter

International Nuclear Information System (INIS)

Boulfelfel, D.; Rangayyan, R.M.; Kuduvalli, G.R.; Hahn, L.J.; Kloiber, R.

1994-01-01

The discrete filtered backprojection (DFBP) algorithm used for the reconstruction of single photon emission computed tomography (SPECT) images affects image quality because of the operations of filtering and discretization. The discretization of the filtered backprojection process can cause the modulation transfer function (MTF) of the SPECT imaging system to be anisotropic and nonstationary, especially near the edges of the camera's field of view. The use of shift-invariant restoration techniques fails to restore large images because these techniques do not account for such variations in the MTF. This study presents the application of a two-dimensional (2-D) shift-variant Kalman filter for post-reconstruction restoration of SPECT slices. This filter was applied to SPECT images of a hollow cylinder phantom; a resolution phantom; and a large, truncated cone phantom containing two types of cold spots, a sphere, and a triangular prism. The images were acquired on an ADAC GENESYS camera. A comparison was performed between results obtained by the Kalman filter and those obtained by shift-invariant filters. Quantitative analysis of the restored images performed through measurement of root mean squared errors shows a considerable reduction in error of Kalman-filtered images over images restored using shift-invariant methods

Anti-smooth muscle antibody

Science.gov (United States)

... gov/ency/article/003531.htm Anti-smooth muscle antibody To use the sharing features on this page, please enable JavaScript. Anti-smooth muscle antibody is a blood test that detects the presence ...

Smooth functors vs. differential forms

NARCIS (Netherlands)

Schreiber, U.; Waldorf, K.

2011-01-01

We establish a relation between smooth 2-functors defined on the path 2-groupoid of a smooth manifold and differential forms on this manifold. This relation can be understood as a part of a dictionary between fundamental notions from category theory and differential geometry. We show that smooth

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.

Adaptive Kalman filtering for diagnosis of multiple component degradations

International Nuclear Information System (INIS)

Aumeier, S. E.; Alpay, B.; Lee, J. C.

2005-01-01

We have developed an adaptive Kalman filtering algorithm for the diagnosis of faults or degradations of multiple components in nuclear power plants. We propose to detect the presence and magnitude of the fault(s) through noisy system observations when the measurements indicate significant deviations from predictions. Our diagnostic algorithm uses the measurement residuals, i.e., the difference between the measurements and predictions, to generate a noise input to the uncertain component state in an adaptive Kalman filtering algorithm so that various postulated component transitions or degradations may be statistically represented. The diagnostic algorithm has been tested with a balance of plant (BOP) model of a boiling water reactor (BWR). We have presented a set of algorithms for the detection and diagnosis of component faults of arbitrary magnitude and type within a multi-component system. By analyzing a number of transients including the one example illustrated in the paper, we find that these algorithms are not only capable of determining the correct component fault and magnitude for single components but also they can be used to determine binary faults satisfactorily. Additional study is under way to evaluate the performance of the proposed algorithm including the sensitivity of the diagnostic time to adaptive noise matrix introduced (see equations 7 and 8 illustrated in the paper)

Temperature profile retrievals with extended Kalman-Bucy filters

Science.gov (United States)

Ledsham, W. H.; Staelin, D. H.

1979-01-01

The Extended Kalman-Bucy Filter is a powerful technique for estimating non-stationary random parameters in situations where the received signal is a noisy non-linear function of those parameters. A practical causal filter for retrieving atmospheric temperature profiles from radiances observed at a single scan angle by the Scanning Microwave Spectrometer (SCAMS) carried on the Nimbus 6 satellite typically shows approximately a 10-30% reduction in rms error about the mean at almost all levels below 70 mb when compared with a regression inversion.

Tsunami Modeling and Prediction Using a Data Assimilation Technique with Kalman Filters

Science.gov (United States)

Barnier, G.; Dunham, E. M.

2016-12-01

Earthquake-induced tsunamis cause dramatic damages along densely populated coastlines. It is difficult to predict and anticipate tsunami waves in advance, but if the earthquake occurs far enough from the coast, there may be enough time to evacuate the zones at risk. Therefore, any real-time information on the tsunami wavefield (as it propagates towards the coast) is extremely valuable for early warning systems. After the 2011 Tohoku earthquake, a dense tsunami-monitoring network (S-net) based on cabled ocean-bottom pressure sensors has been deployed along the Pacific coast in Northeastern Japan. Maeda et al. (GRL, 2015) introduced a data assimilation technique to reconstruct the tsunami wavefield in real time by combining numerical solution of the shallow water wave equations with additional terms penalizing the numerical solution for not matching observations. The penalty or gain matrix is determined though optimal interpolation and is independent of time. Here we explore a related data assimilation approach using the Kalman filter method to evolve the gain matrix. While more computationally expensive, the Kalman filter approach potentially provides more accurate reconstructions. We test our method on a 1D tsunami model derived from the Kozdon and Dunham (EPSL, 2014) dynamic rupture simulations of the 2011 Tohoku earthquake. For appropriate choices of model and data covariance matrices, the method reconstructs the tsunami wavefield prior to wave arrival at the coast. We plan to compare the Kalman filter method to the optimal interpolation method developed by Maeda et al. (GRL, 2015) and then to implement the method for 2D.

Accelerated dynamic cardiac MRI exploiting sparse-Kalman-smoother self-calibration and reconstruction (k  −  t SPARKS)

International Nuclear Information System (INIS)

Park, Suhyung; Park, Jaeseok

2015-01-01

Accelerated dynamic MRI, which exploits spatiotemporal redundancies in k  −  t space and coil dimension, has been widely used to reduce the number of signal encoding and thus increase imaging efficiency with minimal loss of image quality. Nonetheless, particularly in cardiac MRI it still suffers from artifacts and amplified noise in the presence of time-drifting coil sensitivity due to relative motion between coil and subject (e.g. free breathing). Furthermore, a substantial number of additional calibrating signals is to be acquired to warrant accurate calibration of coil sensitivity. In this work, we propose a novel, accelerated dynamic cardiac MRI with sparse-Kalman-smoother self-calibration and reconstruction (k  −  t SPARKS), which is robust to time-varying coil sensitivity even with a small number of calibrating signals. The proposed k  −  t SPARKS incorporates Kalman-smoother self-calibration in k  −  t space and sparse signal recovery in x  −   f space into a single optimization problem, leading to iterative, joint estimation of time-varying convolution kernels and missing signals in k  −  t space. In the Kalman-smoother calibration, motion-induced uncertainties over the entire time frames were included in modeling state transition while a coil-dependent noise statistic in describing measurement process. The sparse signal recovery iteratively alternates with the self-calibration to tackle the ill-conditioning problem potentially resulting from insufficient calibrating signals. Simulations and experiments were performed using both the proposed and conventional methods for comparison, revealing that the proposed k  −  t SPARKS yields higher signal-to-error ratio and superior temporal fidelity in both breath-hold and free-breathing cardiac applications over all reduction factors. (paper)

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...

Distance parameterization for efficient seismic history matching with the ensemble Kalman Filter

NARCIS (Netherlands)

Leeuwenburgh, O.; Arts, R.

2012-01-01

The Ensemble Kalman Filter (EnKF), in combination with travel-time parameterization, provides a robust and flexible method for quantitative multi-model history matching to time-lapse seismic data. A disadvantage of the parameterization in terms of travel-times is that it requires simulation of

Trajectory generation algorithm for smooth movement of a hybrid-type robot Rocker-Pillar

Energy Technology Data Exchange (ETDEWEB)

Jung, Seung Min; Choi, Dong Kyu; Kim, Jong Won [School of Mechanical and Aerospace Engineering, Seoul National University, Seoul (Korea, Republic of); Kim, Hwa Soo [Dept. of Mechanical System Engineering, Kyonggi University, Suwon (Korea, Republic of)

2016-11-15

While traveling on rough terrain, smooth movement of a mobile robot plays an important role in carrying out the given tasks successfully. This paper describes the trajectory generation algorithm for smooth movement of hybrid-type mobile robot Rocker-Pillar by adjusting the angular velocity of its caterpillar as well as each wheel velocity in such a manner to minimize a proper index for smoothness. To this end, a new Smoothness index (SI) is first suggested to evaluate the smoothness of movement of Rocker-Pillar. Then, the trajectory generation algorithm is proposed to reduce the undesired oscillations of its Center of mass (CoM). The experiment are performed to examine the movement of Rocker-Pillar climbing up the step whose height is twice larger than its wheel radius. It is verified that the resulting SI is improved by more than 40 % so that the movement of Rocker-Pillar becomes much smoother by the proposed trajectory algorithm.

Full-waveform inversion using a nonlinearly smoothed wavefield

KAUST Repository

Li, Yuanyuan

2017-12-08

Conventional full-waveform inversion (FWI) based on the least-squares misfit function faces problems in converging to the global minimum when using gradient methods because of the cycle-skipping phenomena. An initial model producing data that are at most a half-cycle away from the observed data is needed for convergence to the global minimum. Low frequencies are helpful in updating low-wavenumber components of the velocity model to avoid cycle skipping. However, low enough frequencies are usually unavailable in field cases. The multiplication of wavefields of slightly different frequencies adds artificial low-frequency components in the data, which can be used for FWI to generate a convergent result and avoid cycle skipping. We generalize this process by multiplying the wavefield with itself and then applying a smoothing operator to the multiplied wavefield or its square to derive the nonlinearly smoothed wavefield, which is rich in low frequencies. The global correlation-norm-based objective function can mitigate the dependence on the amplitude information of the nonlinearly smoothed wavefield. Therefore, we have evaluated the use of this objective function when using the nonlinearly smoothed wavefield. The proposed objective function has much larger convexity than the conventional objective functions. We calculate the gradient of the objective function using the adjoint-state technique, which is similar to that of the conventional FWI except for the adjoint source. We progressively reduce the smoothing width applied to the nonlinear wavefield to naturally adopt the multiscale strategy. Using examples on the Marmousi 2 model, we determine that the proposed FWI helps to generate convergent results without the need for low-frequency information.

Reduction of Used Memory Ensemble Kalman Filtering (RumEnKF): A data assimilation scheme for memory intensive, high performance computing

Science.gov (United States)

Hut, Rolf; Amisigo, Barnabas A.; Steele-Dunne, Susan; van de Giesen, Nick

2015-12-01

Reduction of Used Memory Ensemble Kalman Filtering (RumEnKF) is introduced as a variant on the Ensemble Kalman Filter (EnKF). RumEnKF differs from EnKF in that it does not store the entire ensemble, but rather only saves the first two moments of the ensemble distribution. In this way, the number of ensemble members that can be calculated is less dependent on available memory, and mainly on available computing power (CPU). RumEnKF is developed to make optimal use of current generation super computer architecture, where the number of available floating point operations (flops) increases more rapidly than the available memory and where inter-node communication can quickly become a bottleneck. RumEnKF reduces the used memory compared to the EnKF when the number of ensemble members is greater than half the number of state variables. In this paper, three simple models are used (auto-regressive, low dimensional Lorenz and high dimensional Lorenz) to show that RumEnKF performs similarly to the EnKF. Furthermore, it is also shown that increasing the ensemble size has a similar impact on the estimation error from the three algorithms.

An Unscented Kalman-Particle Hybrid Filter for Space Object Tracking

Science.gov (United States)

Raihan A. V, Dilshad; Chakravorty, Suman

2018-03-01

Optimal and consistent estimation of the state of space objects is pivotal to surveillance and tracking applications. However, probabilistic estimation of space objects is made difficult by the non-Gaussianity and nonlinearity associated with orbital mechanics. In this paper, we present an unscented Kalman-particle hybrid filtering framework for recursive Bayesian estimation of space objects. The hybrid filtering scheme is designed to provide accurate and consistent estimates when measurements are sparse without incurring a large computational cost. It employs an unscented Kalman filter (UKF) for estimation when measurements are available. When the target is outside the field of view (FOV) of the sensor, it updates the state probability density function (PDF) via a sequential Monte Carlo method. The hybrid filter addresses the problem of particle depletion through a suitably designed filter transition scheme. To assess the performance of the hybrid filtering approach, we consider two test cases of space objects that are assumed to undergo full three dimensional orbital motion under the effects of J 2 and atmospheric drag perturbations. It is demonstrated that the hybrid filters can furnish fast, accurate and consistent estimates outperforming standard UKF and particle filter (PF) implementations.

Gossip and Distributed Kalman Filtering: Weak Consensus Under Weak Detectability

Science.gov (United States)

Kar, Soummya; Moura, José M. F.

2011-04-01

The paper presents the gossip interactive Kalman filter (GIKF) for distributed Kalman filtering for networked systems and sensor networks, where inter-sensor communication and observations occur at the same time-scale. The communication among sensors is random; each sensor occasionally exchanges its filtering state information with a neighbor depending on the availability of the appropriate network link. We show that under a weak distributed detectability condition: 1. the GIKF error process remains stochastically bounded, irrespective of the instability properties of the random process dynamics; and 2. the network achieves \\emph{weak consensus}, i.e., the conditional estimation error covariance at a (uniformly) randomly selected sensor converges in distribution to a unique invariant measure on the space of positive semi-definite matrices (independent of the initial state.) To prove these results, we interpret the filtered states (estimates and error covariances) at each node in the GIKF as stochastic particles with local interactions. We analyze the asymptotic properties of the error process by studying as a random dynamical system the associated switched (random) Riccati equation, the switching being dictated by a non-stationary Markov chain on the network graph.

Unscented Kalman filtering in the additive noise case

Institute of Scientific and Technical Information of China (English)

无

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.

ECG fiducial point extraction using switching Kalman filter.

Science.gov (United States)

Akhbari, Mahsa; Ghahjaverestan, Nasim Montazeri; Shamsollahi, Mohammad B; Jutten, Christian

2018-04-01

In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regressive models. In the proposed method, a discrete state variable called "switch" is considered that affects only the observation equations. We denote a mode as a specific observation equation and switch changes between 7 modes and corresponds to different segments of an ECG beat. At each time instant, the probability of each mode is calculated and compared among two consecutive modes and a path is estimated, which shows the relation of each part of the ECG signal to the mode with the maximum probability. ECG FPs are found from the estimated path. For performance evaluation, the Physionet QT database is used and the proposed method is compared with methods based on wavelet transform, partially collapsed Gibbs sampler (PCGS) and extended Kalman filter. For our proposed method, the mean error and the root mean square error across all FPs are 2 ms (i.e. less than one sample) and 14 ms, respectively. These errors are significantly smaller than those obtained using other methods. The proposed method achieves lesser RMSE and smaller variability with respect to others. Copyright © 2018 Elsevier B.V. All rights reserved.

Resveratrol Increases Serum BDNF Concentrations and Reduces Vascular Smooth Muscle Cells Contractility via a NOS-3-Independent Mechanism

Directory of Open Access Journals (Sweden)

Michał Wiciński

2017-01-01

Full Text Available Resveratrol is a polyphenol that presents both antineuroinflammatory properties and the ability to interact with NOS-3, what contributes to vasorelaxation. Brain-derived neurotrophic factor (BNDF, a molecule associated with neuroprotection in many neurodegenerative disorders, is considered as an important element of maintaining stable cerebral blood flow. Vascular smooth muscle cells (VSMCs are considered to be an important element in the pathogenesis of neurodegeneration and a potential preventative target by agents which reduce the contractility of the vessels. Our main objectives were to define the relationship between serum and long-term oral resveratrol administration in the rat model, as well as to assess the effect of resveratrol on phenylephrine- (PHE- induced contraction of vascular smooth muscle cells (VSMCs. Moreover, we attempt to define the dependence of contraction mechanisms on endothelial NO synthase. Experiments were performed on Wistar rats (n=17 pretreated with resveratrol (4 weeks; 10 mg/kg p.o. or placebo. Serum BDNF levels were quantified after 2 and 4 weeks of treatment with ELISA. Contraction force was measured on isolated and perfused tail arteries as the increase of perfusion pressure with a constant flow. Values of serum BNDF in week 0 were 1.18±0.12 ng/mL (treated and 1.17±0.13 ng/mL (control (p = ns. After 2 weeks of treatment, BDNF in the treatment group was higher than in controls, 1.52±0.23 ng/mL and 1.24±0.13 ng/mL, respectively. (p=0.02 Following 4 weeks of treatment, BDNF values were higher in the resveratrol group compared to control 1.64±0.31 ng/mL and 1.32±0.26 ng/mL, respectively (p=0.031. EC50 values obtained for PHE in resveratrol pretreated arteries were significantly higher than controls (5.33±1.7 × 10−7 M/L versus 4.53±1.2 × 10−8 M/L, p<0.05. These results show a significant increase in BDNF concentration in the resveratrol pretreated group. The reactivity of resistant

An adaptive segment method for smoothing lidar signal based on noise estimation

Science.gov (United States)

Wang, Yuzhao; Luo, Pingping

2014-10-01

An adaptive segmentation smoothing method (ASSM) is introduced in the paper to smooth the signal and suppress the noise. In the ASSM, the noise is defined as the 3σ of the background signal. An integer number N is defined for finding the changing positions in the signal curve. If the difference of adjacent two points is greater than 3Nσ, the position is recorded as an end point of the smoothing segment. All the end points detected as above are recorded and the curves between them will be smoothed separately. In the traditional method, the end points of the smoothing windows in the signals are fixed. The ASSM creates changing end points in different signals and the smoothing windows could be set adaptively. The windows are always set as the half of the segmentations and then the average smoothing method will be applied in the segmentations. The Iterative process is required for reducing the end-point aberration effect in the average smoothing method and two or three times are enough. In ASSM, the signals are smoothed in the spacial area nor frequent area, that means the frequent disturbance will be avoided. A lidar echo was simulated in the experimental work. The echo was supposed to be created by a space-born lidar (e.g. CALIOP). And white Gaussian noise was added to the echo to act as the random noise resulted from environment and the detector. The novel method, ASSM, was applied to the noisy echo to filter the noise. In the test, N was set to 3 and the Iteration time is two. The results show that, the signal could be smoothed adaptively by the ASSM, but the N and the Iteration time might be optimized when the ASSM is applied in a different lidar.

GPS Signal Offset Detection and Noise Strength Estimation in a Parallel Kalman Filter Algorithm

National Research Council Canada - National Science Library

Vanek, Barry

1999-01-01

.... The variance of the noise process is estimated and provided to the second algorithm, a parallel Kalman filter structure, which then adapts to changes in the real-world measurement noise strength...

Spacecraft Trajectory Estimation Using a Sampled-Data Extended Kalman Filter with Range-Only Measurements

National Research Council Canada - National Science Library

Erwin, R. S; Bernstein, Dennis S

2005-01-01

.... In this paper we use a sampled-data extended Kalman Filter to estimate the trajectory or a target satellite when only range measurements are available from a constellation or orbiting spacecraft...

Smooth halos in the cosmic web

Energy Technology Data Exchange (ETDEWEB)

Gaite, José, E-mail: jose.gaite@upm.es [Physics Dept., ETSIAE, IDR, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, E-28040 Madrid (Spain)

2015-04-01

Dark matter halos can be defined as smooth distributions of dark matter placed in a non-smooth cosmic web structure. This definition of halos demands a precise definition of smoothness and a characterization of the manner in which the transition from smooth halos to the cosmic web takes place. We introduce entropic measures of smoothness, related to measures of inequality previously used in economy and with the advantage of being connected with standard methods of multifractal analysis already used for characterizing the cosmic web structure in cold dark matter N-body simulations. These entropic measures provide us with a quantitative description of the transition from the small scales portrayed as a distribution of halos to the larger scales portrayed as a cosmic web and, therefore, allow us to assign definite sizes to halos. However, these ''smoothness sizes'' have no direct relation to the virial radii. Finally, we discuss the influence of N-body discreteness parameters on smoothness.

Smooth halos in the cosmic web

International Nuclear Information System (INIS)

Gaite, José

2015-01-01

Dark matter halos can be defined as smooth distributions of dark matter placed in a non-smooth cosmic web structure. This definition of halos demands a precise definition of smoothness and a characterization of the manner in which the transition from smooth halos to the cosmic web takes place. We introduce entropic measures of smoothness, related to measures of inequality previously used in economy and with the advantage of being connected with standard methods of multifractal analysis already used for characterizing the cosmic web structure in cold dark matter N-body simulations. These entropic measures provide us with a quantitative description of the transition from the small scales portrayed as a distribution of halos to the larger scales portrayed as a cosmic web and, therefore, allow us to assign definite sizes to halos. However, these ''smoothness sizes'' have no direct relation to the virial radii. Finally, we discuss the influence of N-body discreteness parameters on smoothness

Advantages in using Kalman phasor estimation in numerical differential protective relaying compared to the Fourier estimation method

DEFF Research Database (Denmark)

Bukh, Bjarne; Gudmundsdottir, Unnur Stella; Balle Holst, Per

2007-01-01

This paper demonstrates the results obtained from detailed studies of Kalman phasor estimation used in numerical differential protective relaying of a power transformer. The accuracy and expeditiousness of a current estimate in the numerical differential protection is critical for correct...... and not unnecessary activation of the breakers by the protecting relay, and the objective of the study was to utilize the capability of Kalman phasor estimation in a signal model representing the expected current signal from the current transformers of the power transformer. The used signal model included...

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.

Cubature/ Unscented/ Sigma Point Kalman Filtering with Angular Measurement Models

Science.gov (United States)

2015-07-06

similarly transformed to work with the Laplace distribution. Cubature formulae for w(x) = 1 over regions of various shapes could be used for evaluating...measurement and process non- linearities, such as the cubature Kalman filter, can perform ex- tremely poorly in many applications involving angular...in the form of the “unscented transform ”) consider just converting such measurements into Cartesian coordinates and feeding the converted measurements

Recursive B-spline approximation using the Kalman filter

Directory of Open Access Journals (Sweden)

Jens Jauch

2017-02-01

Full Text Available This paper proposes a novel recursive B-spline approximation (RBA algorithm which approximates an unbounded number of data points with a B-spline function and achieves lower computational effort compared with previous algorithms. Conventional recursive algorithms based on the Kalman filter (KF restrict the approximation to a bounded and predefined interval. Conversely RBA includes a novel shift operation that enables to shift estimated B-spline coefficients in the state vector of a KF. This allows to adapt the interval in which the B-spline function can approximate data points during run-time.

Distributed Dynamic State Estimation with Extended Kalman Filter

Energy Technology Data Exchange (ETDEWEB)

Du, Pengwei; Huang, Zhenyu; Sun, Yannan; Diao, Ruisheng; Kalsi, Karanjit; Anderson, Kevin K.; Li, Yulan; Lee, Barry

2011-08-04

Increasing complexity associated with large-scale renewable resources and novel smart-grid technologies necessitates real-time monitoring and control. Our previous work applied the extended Kalman filter (EKF) with the use of phasor measurement data (PMU) for dynamic state estimation. However, high computation complexity creates significant challenges for real-time applications. In this paper, the problem of distributed dynamic state estimation is investigated. One domain decomposition method is proposed to utilize decentralized computing resources. The performance of distributed dynamic state estimation is tested on a 16-machine, 68-bus test system.

Analysis of a Kalman filter based method for on-line estimation of atmospheric dispersion parameters using radiation monitoring data

DEFF Research Database (Denmark)

Drews, Martin; Lauritzen, Bent; Madsen, Henrik

2005-01-01

A Kalman filter method is discussed for on-line estimation of radioactive release and atmospheric dispersion from a time series of off-site radiation monitoring data. The method is based on a state space approach, where a stochastic system equation describes the dynamics of the plume model...... parameters, and the observables are linked to the state variables through a static measurement equation. The method is analysed for three simple state space models using experimental data obtained at a nuclear research reactor. Compared to direct measurements of the atmospheric dispersion, the Kalman filter...... estimates are found to agree well with the measured parameters, provided that the radiation measurements are spread out in the cross-wind direction. For less optimal detector placement it proves difficult to distinguish variations in the source term and plume height; yet the Kalman filter yields consistent...

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.

Flatness-based control and Kalman filtering for a continuous-time macroeconomic model

Science.gov (United States)

Rigatos, G.; Siano, P.; Ghosh, T.; Busawon, K.; Binns, R.

2017-11-01

The article proposes flatness-based control for a nonlinear macro-economic model of the UK economy. The differential flatness properties of the model are proven. This enables to introduce a transformation (diffeomorphism) of the system's state variables and to express the state-space description of the model in the linear canonical (Brunowsky) form in which both the feedback control and the state estimation problem can be solved. For the linearized equivalent model of the macroeconomic system, stabilizing feedback control can be achieved using pole placement methods. Moreover, to implement stabilizing feedback control of the system by measuring only a subset of its state vector elements the Derivative-free nonlinear Kalman Filter is used. This consists of the Kalman Filter recursion applied on the linearized equivalent model of the financial system and of an inverse transformation that is based again on differential flatness theory. The asymptotic stability properties of the control scheme are confirmed.

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.

Comparison of some nonlinear smoothing methods

International Nuclear Information System (INIS)

Bell, P.R.; Dillon, R.S.

1977-01-01

Due to the poor quality of many nuclear medicine images, computer-driven smoothing procedures are frequently employed to enhance the diagnostic utility of these images. While linear methods were first tried, it was discovered that nonlinear techniques produced superior smoothing with little detail suppression. We have compared four methods: Gaussian smoothing (linear), two-dimensional least-squares smoothing (linear), two-dimensional least-squares bounding (nonlinear), and two-dimensional median smoothing (nonlinear). The two dimensional least-squares procedures have yielded the most satisfactorily enhanced images, with the median smoothers providing quite good images, even in the presence of widely aberrant points

CD16(+) monocytes with smooth muscle cell characteristics are reduced in human renal chronic transplant dysfunction

NARCIS (Netherlands)

Boersema, M.; van den Born, Joost; van Ark, J.; Harms, Geertruida; Seelen, M. A.; van Dijk, M. C. R. F.; van Goor, H.; Navis, G. J.; Popa, E. R.; Hillebrands, J. L.

In chronic transplant dysfunction (CTD), persistent (allo)immune-mediated inflammation eventually leads to tissue remodeling including neointima formation in intragraft arteries. We previously showed that recipient-derived neointimal alpha-SMA(+) smooth muscle-like cells are present in human renal

Smooth Nanowire/Polymer Composite Transparent Electrodes

KAUST Repository

Gaynor, Whitney; Burkhard, George F.; McGehee, Michael D.; Peumans, Peter

2011-01-01

Smooth composite transparent electrodes are fabricated via lamination of silver nanowires into the polymer poly-(4,3-ethylene dioxythiophene): poly(styrene-sulfonate) (PEDOT:PSS). The surface roughness is dramatically reduced compared to bare nanowires. High-efficiency P3HT:PCBM organic photovoltaic cells can be fabricated using these composites, reproducing the performance of cells on indium tin oxide (ITO) on glass and improving the performance of cells on ITO on plastic. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Smooth Nanowire/Polymer Composite Transparent Electrodes

KAUST Repository

Gaynor, Whitney

2011-04-29

Smooth composite transparent electrodes are fabricated via lamination of silver nanowires into the polymer poly-(4,3-ethylene dioxythiophene): poly(styrene-sulfonate) (PEDOT:PSS). The surface roughness is dramatically reduced compared to bare nanowires. High-efficiency P3HT:PCBM organic photovoltaic cells can be fabricated using these composites, reproducing the performance of cells on indium tin oxide (ITO) on glass and improving the performance of cells on ITO on plastic. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Towards real-time diffuse optical tomography for imaging brain functions cooperated with Kalman estimator

Science.gov (United States)

Wang, Bingyuan; Zhang, Yao; Liu, Dongyuan; Ding, Xuemei; Dan, Mai; Pan, Tiantian; Wang, Yihan; Li, Jiao; Zhou, Zhongxing; Zhang, Limin; Zhao, Huijuan; Gao, Feng

2018-02-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging method to monitor the cerebral hemodynamic through the optical changes measured at the scalp surface. It has played a more and more important role in psychology and medical imaging communities. Real-time imaging of brain function using NIRS makes it possible to explore some sophisticated human brain functions unexplored before. Kalman estimator has been frequently used in combination with modified Beer-Lamber Law (MBLL) based optical topology (OT), for real-time brain function imaging. However, the spatial resolution of the OT is low, hampering the application of OT in exploring some complicated brain functions. In this paper, we develop a real-time imaging method combining diffuse optical tomography (DOT) and Kalman estimator, much improving the spatial resolution. Instead of only presenting one spatially distributed image indicating the changes of the absorption coefficients at each time point during the recording process, one real-time updated image using the Kalman estimator is provided. Its each voxel represents the amplitude of the hemodynamic response function (HRF) associated with this voxel. We evaluate this method using some simulation experiments, demonstrating that this method can obtain more reliable spatial resolution images. Furthermore, a statistical analysis is also conducted to help to decide whether a voxel in the field of view is activated or not.

On-line structural response analysis: using the extended Kalman estimator/identifier

International Nuclear Information System (INIS)

Candy, J.V.

1979-01-01

This report disucsses the development of on-line state and parameter estimators used to analyze the structural response of buildings. The estimator/identifier is an extended Kalman filter (EKF), which has been applied with great success in other technological areas. It is shown that the EKF can perform quite well on simulated noisy structural response data

Kalman filtration of radiation monitoring data from atmospheric dispersion of radioactive materials

DEFF Research Database (Denmark)

Drews, M.; Lauritzen, B.; Madsen, H.

2004-01-01

A Kalman filter method using off-site radiation monitoring data is proposed as a tool for on-line estimation of the source term for short-range atmospheric dispersion of radioactive materials. The method is based on the Gaussian plume model, in which the plume parameters including the source term...

Control of motion stability of the line tracer robot using fuzzy logic and kalman filter

Science.gov (United States)

Novelan, M. S.; Tulus; Zamzami, E. M.

2018-03-01

Setting of motion and balance line tracer robot two wheels is actually a combination of a two-wheeled robot balance concept and the concept of line follower robot. The main objective of this research is to maintain the robot in an upright and can move to follow the line of the Wizard while maintaining balance. In this study the motion balance system on line tracer robot by considering the presence of a noise, so that it takes the estimator is used to mengestimasi the line tracer robot motion. The estimation is done by the method of Kalman Filter and the combination of Fuzzy logic-Fuzzy Kalman Filter called Kalman Filter, as well as optimal smooting. Based on the results of the study, the value of the output of the fuzzy results obtained from the sensor input value has been filtered before entering the calculation of the fuzzy. The results of the output of the fuzzy logic hasn’t been able to control dc motors are well balanced at the moment to be able to run. The results of the fuzzy logic by using membership function of triangular membership function or yet can control with good dc motor movement in order to be balanced

Skew redundant MEMS IMU calibration using a Kalman filter

International Nuclear Information System (INIS)

Jafari, M; Sahebjameyan, M; Moshiri, B; Najafabadi, T A

2015-01-01

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. (paper)