Regularized Adaptive Notch Filters for Acoustic Howling Suppression
Gil-Cacho, Pepe; van Waterschoot, Toon; Moonen, Marc;
2009-01-01
In this paper, a method for the suppression of acoustic howling is developed, based on adaptive notch filters (ANF) with regularization (RANF). The method features three RANFs working in parallel to achieve frequency tracking, howling detection and suppression. The ANF-based approach to howling...... components in the source signal are not affected. The RANFs proposed in this paper are implemented in direct form and are updated using a gradient descent type algorithm. Results show that, under certain conditions, the level of suppression and sound quality is similar to what is obtained with frame...
Fast and Flexible Tracking and Mitigating a Jamming Signal with an Adaptive Notch Filter
BORIO DANIELE; O'DRISCOLL CILLIAN; Fortuny Guasch, Joaquim
2014-01-01
GNSS jammers are small portable devices able to broadcast disruptive interference and overpower the much weaker GNSS signals. The authors consider the use of an adaptive notch filter as an effective solution for mitigating jamming effects.
The purpose of this study was to compare the monopolar electromyographic (EMG) amplitude versus isometric force relationships from three signal processing methods (raw versus notch filtering versus adaptive filtering). Seventeen healthy subjects (mean ± SD age = 24.6 ± 4.3 yr) performed incremental isometric muscle actions of the dominant leg extensors in 10% increments from 10% to 100% of the maximum voluntary contraction (MVC). During each muscle action, a monopolar surface EMG signal was recorded from the vastus lateralis and processed with the three signal processing methods. The linear slope coefficients for the EMG amplitude versus isometric force relationships were equivalent for the three signal processing methods and correlated (r = 0.997–0.999). However, the mean amplitude values for the notch-filtered signals were less than those for the raw and adaptive-filtered signals. Thus, adaptive filtering may be the best method for removing electromagnetic noise from monopolar surface EMG signals
Automatic balancing of AMB systems using plural notch filter and adaptive synchronous compensation
Xu, Xiangbo; Chen, Shao; Zhang, Yanan
2016-07-01
To achieve automatic balancing in active magnetic bearing (AMB) system, a control method with notch filters and synchronous compensators is widely employed. However, the control precision is significantly affected by the synchronous compensation error, which is caused by parameter errors and variations of the power amplifiers. Furthermore, the computation effort may become intolerable if a 4-degree-of-freedom (dof) AMB system is studied. To solve these problems, an adaptive automatic balancing control method in the AMB system is presented in this study. Firstly, a 4-dof radial AMB system is described and analyzed. To simplify the controller design, the 4-dof dynamic equations are transferred into two plural functions related to translation and rotation, respectively. Next, to achieve automatic balancing of the AMB system, two synchronous equations are formed. Solution of them leads to a control strategy based on notch filters and feedforward controllers with an inverse function of the power amplifier. The feedforward controllers can be simplified as synchronous phases and amplitudes. Then, a plural phase-shift notch filter which can identify the synchronous components in 2-dof motions is formulated, and an adaptive compensation method that can form two closed-loop systems to tune the synchronous amplitude of the feedforward controller and the phase of the plural notch filter is proposed. Finally, the proposed control strategy is verified by both simulations and experiments on a test rig of magnetically suspended control moment gyro. The results indicate that this method can fulfill the automatic balancing of the AMB system with a light computational load.
Zheng, Shiqiang; Feng, Rui
2016-03-01
This paper introduces a feedforward control strategy combined with a novel adaptive notch filter to solve the problem of rotor imbalance in high-speed Magnetically Suspended Centrifugal Compressors (MSCCs). Unbalance vibration force of rotor in MSCC is mainly composed of current stiffness force and displacement stiffness force. In this paper, the mathematical model of the unbalance vibration with the proportional-integral-derivative (PID) control laws is presented. In order to reduce the unbalance vibration, a novel adaptive notch filter is proposed to identify the synchronous frequency displacement of the rotor as a compensation signal to eliminate the current stiffness force. In addition, a feedforward channel from position component to control output is introduced to compensate displacement stiffness force to achieve a better performance. A simplified inverse model of power amplifier is included in the feedforward channel to reject the degrade performance caused by its low-pass characteristic. Simulation and experimental results on a MSCC demonstrate a significant effect on the synchronous vibration suppression of the magnetically suspended rotor at a high speed.
Park, Chanki; Lee, Boreom
2014-01-01
Background Many researchers have attempted to acquire respiratory rate (RR) information from a photoplethysmogram (PPG) because respiration affects the waveform of the PPG. However, most of these methods were difficult to operate in real-time because of their complexity or computational requirements. From these needs, we attempted to develop a method to estimate RR from a PPG with a light computational burden. Methods To obtain RR information, we adopt a sequential filtering structure and fre...
Wu, Shang-Teh; Lian, Sing-Han; Chen, Sheng-Han
2015-07-01
For a low-stiffness beam driven by a ball-screw stage, the lateral vibrations cannot be adequately controlled by a collocated compensator based on rotary-encoder feedback alone. Acceleration signals at the tip of the flexible beam are measured for active vibration control in addition to the collocated compensator. A second-order bandpass filter (a line enhancer) and two notch filters are included in the acceleration-feedback loop to raise modal dampings for the first and the second flexible modes without exciting higher-frequency resonances. A novel adaptation algorithm is devised to tune the center frequencies of the notch filters in real time. It consists of a second-order low-pass filter, a second-order bandpass filter and a phase detector. Improvement of the control system is elaborated progressively with the root-locus and bode-plot analyses, along with a physical interpretation. Extensive testings are conducted on an experimental device to verify the effectiveness of the control method.
Compact microstrip bandpass filter with tunable notch
Christensen, Silas; Zhurbenko, Vitaliy; Johansen, Tom Keinicke
2014-01-01
Two different designs combining a bandpass and a notch filter are developed to operate in the receiving band from 350–470 MHz. The bandpass filter is designed from a simple structure, by use of only four short circuited stubs and a half wavelength transmission line connecting the stubs. The tunable...... notch filter ensures an attenuation level of 19.3 dB to 27.3 dB in the frequency range from 360–480 MHz. The measured passband ripple of the combined filter is less than 0.5 dB, while the insertion loss for the simplest design is less than 1.7 dB only 10 MHz from the notch frequency. Even though...... the wavelength on the selected substrate (εr = 3.55) is approximately 45 cm, the outer dimensions of the final filter only measure 10×10 cm2....
Digital notch filter based active damping for LCL filters
Yao, Wenli; Yang, Yongheng; Zhang, Xiaobin;
2015-01-01
. In contrast, the active damping does not require any dissipation elements, and thus has become of increasing interest. As a result, a vast of active damping solutions have been reported, among which multi-loop control systems and additional sensors are necessary, leading to increased cost and complexity....... In this paper, a notch filter based active damping without the requirement of additional sensors is proposed, where the inverter current is employed as the feedback variable. Firstly, a design method of the notch filter for active damping is presented. The entire system stability has then been investigated...... in the z-domain. Simulations and experiments are carried out to verify the proposed active damping method. Both results have confirmed that the notch filter based active damping can ensure the entire system stability in the case of resonances with a good system performance....
FM Interference Noise Suppression Based on Adaptive Notch Filter%基于自适应陷波器的噪声调频干扰抑制方法
路翠华; 李国林; 谢鑫
2014-01-01
针对线性调频引信抗噪声干扰能力比较差的问题，提出了基于自适应陷波器的噪声调频干扰抑制方法。该方法根据线性调频引信差频信号的单频特性，将自适应陷波器应用到线性调频引信中，对噪声调频干扰进行抑制。通过自适应调整陷波器的权值，使陷波器在差频信号的频率点具有陷波特性，从而达到噪声调频干扰抑制的目的。仿真结果表明：SJB=-10 dB时，仍然能达到很好的噪声调频干扰抑制效果。%Aiming at the problem that linear frequency-modulated fuze’s ability of anti-noise interference was poor,the method of noise FM interference suppression based on adaptive notch filter was presented.According to the characteristic that the difference frequency signal of linear frequency-modulated fuze was monochromatic, an adaptive notch filter was adopted to suppress FM interference noise in linear frequency-modulated fuze. Through adj usting notch filter’s weights,the notch filter has the notch characteristics in difference frequency signal's frequency,then noise FM interference was suppressed.The simulation results showed that when SJR=-10dB,FM interference noise could be still suppressed effectively.
Notch filter feedback controlled chaos in buck converter
Lu Wei-Guo; Zhou Luo-Wei; Luo Quan-Ming
2007-01-01
A method of controlling chaos in the voltage-mode buck converter is presented by using an improved notch filter feedback control in this paper. The proposed control part comprises a notch filter and a low-pass filter. The discrepancy between the outputs of the two filters is introduced into the control prototype of the power converter. In this way, the system period-1 solution is kept unchanged. The harmonic balance method is applied to analysing the variation law of the system bifurcation point, and then the stable range of the feedback gain is ascertained. The results of simulation and experiment are also given finally.
Broadband notch filter design for millimeter-wave plasma diagnostics
Furtula, Vedran; Michelsen, Poul; Leipold, Frank;
2010-01-01
Notch filters are integrated in plasma diagnostic systems to protect millimeter-wave receivers from intensive stray radiation. Here we present a design of a notch filter with a center frequency of 140 GHz, a rejection bandwidth of ∼ 900 MHz, and a typical insertion loss below 2 dB in the passband...... in the cylindrical cavities is the fundamental TE11. The performance of the constructed filter is measured using a vector network analyzer monitoring a total bandwidth of 30 GHz. We compare the measurements with numerical simulations. © 2010 EURATOM...
Design and Analysis of Robust Active Damping for LCL Filters using Digital Notch Filters
Yao, Wenli; Yang, Yongheng; Zhang, Xiaobin;
2016-01-01
resonance, where the notch frequency should be aligned exactly to the resonant frequency of the LCL filter. However, parameter variations of the LCL filter as well as the time delay appearing in digital control systems will induce resonance drifting, and thus break this alignment, possibly deteriorating the......Resonant poles of LCL filters may challenge the entire system stability especially in digital-controlled Pulse Width Modulation (PWM) inverters. In order to tackle the resonance issues, many active damping solutions have been reported. For instance, a notch filter can be employed to damp the......-connected PWM inverter verify the effectiveness of the proposed design for robust active damping using digital notch filters....
Psaltis, Demetri; Hong, John
1984-01-01
A new adaptive filter utilizing acoustooptic devices in a space integrating architecture is described. Two configurations are presented; one of them, suitable for signal estimation, is shown to approximate the Wiener filter, while the other, suitable for detection, is shown to approximate the matched filter.
A Microwave Photonic Notch Filter Using a Microfiber Ring Resonator
A novel tunable microwave photonic filter based on a microfiber ring resonator is proposed and experimentally demonstrated. A fiber ring laser based on the microfiber ring resonator is employed to generate two single-longitudinal-mode carriers, then the dispersive element introduces the delay between two modulated carriers. By adjusting the diameter of the microfiber ring resonator, the proposed microwave photonic notch filter can be continuously and widely tuned. The measured notch rejection ratio is greater than 35 dB, and there is good agreement between the experimental result and the theoretical analysis. (fundamental areas of phenomenology (including applications))
Ge, Jia; Feng, Hanlin; Scott, Guy; Fok, Mable P
2015-01-01
A high-speed tunable microwave photonic notch filter with ultrahigh rejection ratio is presented, which is achieved by semiconductor optical amplifier (SOA)-based single-sideband modulation and optical spectral filtering with a phase modulator-incorporated Lyot (PM-Lyot) filter. By varying the birefringence of the phase modulator through electro-optic effect, electrically tuning of the microwave photonic notch filter is experimentally achieved at tens of gigahertz speed. The use of SOA-polarizer based single-sideband modulation scheme provides good sideband suppression over a wide frequency range, resulting in an ultrahigh rejection ratio of the microwave photonic notch filter. Stable filter spectrum with bandstop rejection ratio over 60 dB is observed over a frequency tuning range from 1.8 to 10 GHz. Compare with standard interferometric notch filter, narrower bandwidth and sharper notch profile are achieved with the unique PM-Lyot filter, resulting in better filter selectivity. Moreover, bandwidth tuning is also achieved through polarization adjustment inside the PM-Lyot filter, that the 10-dB filter bandwidth is tuned from 0.81 to 1.85 GHz. PMID:25531605
105 GHz Notch Filter Design for Collective Thomson Scattering
Furtula, Vedran; Michelsen, Poul; Leipold, Frank; Johansen, T.; Korsholm, Søren Bang; Meo, Fernando; Moseev, Dmitry; Nielsen, Stefan Kragh; Salewski, Mirko; Stejner Pedersen, Morten
2011-01-01
A millimeter-wave notch filter with 105-GHz center frequency, >20-GHz passband coverage, and 1-GHz rejection bandwidth has been constructed. The design is based on a fundamental rectangular waveguide with cylindrical cavities coupled by narrow iris gaps, i.e., small elongated holes of negligible...
A low-loss, continuously tunable microwave notch filter
Acar, Öncel; Johansen, Tom Keinicke; Zhurbenko, Vitaliy
2016-01-01
The development in high-end microwave transceiver systems toward the software defined radio has brought about the need for tunable frontend filters. Although the problem is being tackled by the microwave community, there still appears to be an unmet demand for practical tunable filter technologies....... With this motivation, this work presents a tuning method that delivers a resonator Q0 of 2000–3621 within a minimum tuning ratio of 1:1.42. A continuously tunable notch filter based on this tuning method is presented. The design is manufactured, measured, and verified. It is shown that the tuning technology compares...
Filter Bank Design for Subband Adaptive Filtering
Haan, Jan Mark de
2001-01-01
Adaptive filtering is an important subject in the field of signal processing and has numerous applications in fields such as speech processing and communications. Examples in speech processing include speech enhancement, echo- and interference- cancellation, and speech coding. Subband filter banks have been introduced in the area of adaptive filtering in order to improve the performance of time domain adaptive filters. The main improvements are faster convergence speed and the reduction of co...
Tunable microwave photonic notch filter based on sliced broadband optical source.
Yu, Yang; Li, Shangyuan; Zheng, Xiaoping; Zhang, Hanyi; Zhou, Bingkun
2015-09-21
A microwave photonic filter is demonstrated with both tunable center frequency and bandwidth. This filter is switchable from all-pass, bandpass to notch filter, and the notch filter is a result of the subtraction of a bandpass filter from an all-pass filter based on a balanced photodetector. The all-pass filter is achieved based on a single wavelength radio over fiber link, and the bandpass one is acquired by using the spectrum-sliced broadband optical source. Theoretical analysis and experimental results show that both the center frequency and the bandwidth of the notch filter can be widely tuned. PMID:26406636
Double-sided printed bow-tie antenna with notch filter for UWB applications
Hirata, Akimasa; ヒラタ, アキマサ; 平田, 晃正
2009-01-01
This letter proposes a double-sided printed bow-tie antenna with a notch band. The notch filter is based on a grounded patch inserted into the feeding microstrip line. The advantage of the structure is its tunability of the notch band.
R P Shukla; Sanjiva Kumar; A K Sinha; Manika Mallick; S Thakur; N K Sahoo
2006-08-01
An indigenously designed and developed micro-Raman spectrograph, consisting of a diode-pumped solid-state green laser for the excitation of Raman scattering, a Raman imaging microscope, CCD as a detector and a notch filter, has been extensively studied to evaluate its performance. A dielectric edge filter (having 27 alternate layers of SiO2 and TiO2) and a holographic notch filter (Oriel make) have been used to block the Rayleigh scattered light from the sample to the entrance slit of the spectrograph. Holographic notch filter is found to be able to record the Raman shifts below 700 cm-1 conveniently whereas dielectric edge filter (27 layers) has enabled the spectrograph to record the Raman spectra very efficiently after a wave-number shift of 700 cm-1. It has also been observed that the instrument using the edge filter provides a peculiar spectrum consisting of three spectral lines having Raman shifts as 569, 1328 and 1393 cm-1 in the Raman spectrum of a weakly scattering sample with large reflectivity. Similarly, a spectrum consisting of multiple lines has been observed when the instrument is being operated using a holographic notch filter. These spectral lines are not observed in the case of liquid samples such as benzene, carbon tetrachloride, ethanol, diethyl ether etc. The origin of these peculiar spectral lines has been briefly discussed in the paper. Additionally, a major motivation for this work is to utilize the results for the selection of an appropriate filter depending on the type of the sample, i.e. weakly scattered and highly reflecting sample or highly scattered and low reflecting sample.
Adaptively robust filtering with classified adaptive factors
CUI Xianqiang; YANG Yuanxi
2006-01-01
The key problems in applying the adaptively robust filtering to navigation are to establish an equivalent weight matrix for the measurements and a suitable adaptive factor for balancing the contributions of the measurements and the predicted state information to the state parameter estimates. In this paper, an adaptively robust filtering with classified adaptive factors was proposed, based on the principles of the adaptively robust filtering and bi-factor robust estimation for correlated observations. According to the constant velocity model of Kalman filtering, the state parameter vector was divided into two groups, namely position and velocity. The estimator of the adaptively robust filtering with classified adaptive factors was derived, and the calculation expressions of the classified adaptive factors were presented. Test results show that the adaptively robust filtering with classified adaptive factors is not only robust in controlling the measurement outliers and the kinematic state disturbing but also reasonable in balancing the contributions of the predicted position and velocity, respectively, and its filtering accuracy is superior to the adaptively robust filter with single adaptive factor based on the discrepancy of the predicted position or the predicted velocity.
Scalable In-Band Optical Notch-Filter Labeling for Ultrahigh Bit Rate Optical Packet Switching
Medhin, Ashenafi Kiros; Galili, Michael; Oxenløwe, Leif Katsuo
2014-01-01
We propose a scalable in-band optical notch-filter labeling scheme for optical packet switching of high-bit-rate data packets. A detailed characterization of the notch-filter labeling scheme and its effect on the quality of the data packet is carried out in simulation and verified by experimental...
Santoso, Isman
2015-01-01
Noiseondigital imagecan be periodicnoisewhich visuallyappearsthere arelines on theimagearespreadevenly. Filtering is one of the mechanism to reduce noise. Periodic noise can be reduced by using Selective Filter. The filter method used by the author to reduce the noise is Optimum Notch Filter and Band Reject Filter which are both a type of selective filter. The use of the filter are processed in the frequency domain, because of that author also use the Fast Fourier Transform method to produce ...
Microwave photonic notch filter based on a dual-Sagnac-loop structure.
Wang, Xudong; Chan, Erwin H W; Minasian, Robert A
2010-11-20
A new single-wavelength, coherence-free microwave photonic notch filter is presented. The concept is based on a dual-Sagnac-loop structure that functions with a new principle in which the two loops operate with different free spectral ranges, and which generate noncommensurate taps. It has the ability to generate a narrow notch response and can operate to high frequencies. Experimental results demonstrate a notch filter with a narrow notch width, a flat passband, and high stop-band attenuation of over 40dB. PMID:21102681
Adaptive filtering and change detection
Gustafsson, Fredrik
2003-01-01
Adaptive filtering is a classical branch of digital signal processing (DSP). Industrial interest in adaptive filtering grows continuously with the increase in computer performance that allows ever more conplex algorithms to be run in real-time. Change detection is a type of adaptive filtering for non-stationary signals and is also the basic tool in fault detection and diagnosis. Often considered as separate subjects Adaptive Filtering and Change Detection bridges a gap in the literature with a unified treatment of these areas, emphasizing that change detection is a natural extensi
Long, Yun; Wang, Jian
2015-07-13
We propose a simple scheme to realize ultra-high peak rejection notch microwave photonic filter (MPF) based on a single silicon microring resonator (MRR). Using the combination of a conventional phase modulator (PM), a tunable bandpass filter (TBF), and a silicon MRR to manipulate the phase and amplitude of optical sidebands resulting in a signal cancellation at the RF notch filter frequency, we experimentally demonstrate a notch MPF with an ultra-high peak rejection beyond 60 dB. The frequency tunability of the proposed ultra-high peak rejection MPF is also demonstrated in the experiment. PMID:26191836
Adaptive filtering prediction and control
Goodwin, Graham C
2009-01-01
Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o
Induction machine condition monitoring using notch-filtered motor current
Günal, Serkan; Gökhan Ece, Dog˜an; Nezih Gerek, Ömer
2009-11-01
This paper presents a new approach to induction motor condition monitoring using notch-filtered motor current signature analysis (NFMCSA). Unlike most of the previous work utilizing motor current signature analysis (MCSA) using spectral methods to extract required features for detecting motor fault conditions, here NFMCSA is performed in time-domain to extract features of energy, sample extrema, and third and fourth cumulants evaluated from data within sliding time window. Six identical three-phase induction motors were used for the experimental verification of the proposed method. One healthy machine was used as a reference, while other five with different synthetic faults were used for condition detection and classification. Extracted features obtained from NFMCSA of all motors were employed in three different and popular classifiers. The proposed motor current analysis and the performance of the features used for fault detection and classification are examined at various motor load levels and it is shown that a successful induction motor condition monitoring system is developed. Developed system is also able to indicate the load level and the type of a fault in multi-dimensional feature space representation. In order to test the generality and applicability of the developed method to other induction motors, data acquired from another healthy induction motor with different number of poles and rated power is also incorporated into the system. In spite of the above difference, the proposed feature set successfully locates the healthy motor within the classification cluster of "healthy motors" on the feature space.
160 Gb/s Raman-assisted notch-filtered XPM wavelength conversion and transmission
Galili, Michael; Oxenløwe, Leif Katsuo; Mulvad, Hans Christian Hansen;
2007-01-01
In-line wavelength conversion of 160 Gb/s data by Raman-assisted notch-filtered XPM is demonstrated for 130 km total transmission. The improvement in system performance from applying Raman gain during conversion is shown.......In-line wavelength conversion of 160 Gb/s data by Raman-assisted notch-filtered XPM is demonstrated for 130 km total transmission. The improvement in system performance from applying Raman gain during conversion is shown....
Frequency agile microwave photonic notch filter with anomalously-high stopband rejection
Marpaung, David; Pant, Ravi; Eggleton, Benjamin J
2013-01-01
We report a novel class microwave photonic (MWP) notch filter with a very narrow isolation bandwidth (10 MHz), an ultrahigh stopband rejection (> 60 dB), a wide frequency tuning (1-30 GHz), and flexible bandwidth reconfigurability (10-65 MHz). This record performance is enabled by a new concept of sidebands amplitude and phase controls using an electro-optic modulator and an optical filter. This new concept enables energy efficient operation in active MWP notch filters, and opens up the pathway to enable low-power nanophotonic devices as high performance RF filters.
In this paper, a continuously tunable microwave photonic notch filter is proposed and experimentally demonstrated. This filter is based on the differential group delay generated by a high-birefringence linearly chirped fiber Bragg grating. This microwave photonic filter belongs to the orthogonal polarization approach, polarization maintaining structure ensures the filter free from the random optical interference problem. Its response is induced by the differential group delay (DGD) of the Hi-Bi LCFBG and it can be varied by tuning the grating through adding gradient strength to the grating. Free spectral range tuning by 9.27 GHz with more than 35 dB notch rejection is achieved.
Si3N4 ring resonator-based microwave photonic notch filter with an ultrahigh peak rejection
Marpaung, David; Pant, Ravi; Roeloffzen, Chris; Leinse, Arne; Hoekman, Marcel; Heideman, Rene; Eggleton, Benjamin J
2013-01-01
We report a simple technique in microwave photonic (MWP) signal processing that allows the use of an optical filter with a shallow notch to exhibit a microwave notch filter with anomalously high rejection level. We implement this technique using a low-loss, tunable Si3N4 optical ring resonator as the optical filter, and achieved an MWP notch filter with an ultra-high peak rejection > 60 dB, a tunable high resolution bandwidth of 247-840 MHz, and notch frequency tuning of 2-8 GHz. To our knowledge, this is a record combined peak rejection and resolution for an integrated MWP filter.
Frequency agile microwave photonic notch filter with anomalously-high stopband rejection
Marpaung, David; Morrison, Blair; Pant, Ravi; Eggleton, Benjamin J.
2013-01-01
We report a novel class microwave photonic (MWP) notch filter with a very narrow isolation bandwidth (10 MHz), an ultrahigh stopband rejection (> 60 dB), a wide frequency tuning (1-30 GHz), and flexible bandwidth reconfigurability (10-65 MHz). This record performance is enabled by a new concept of sidebands amplitude and phase controls using an electro-optic modulator and an optical filter. This new concept enables energy efficient operation in active MWP notch filters, and opens up the pathw...
Widely tunable microwave photonic notch filter based on slow and fast light effects
Xue W.; Sales S.; Mork J.; Capmany J.
2009-01-01
A continuously tunable microwave photonic notch filter at around 30 GHz is experimentally demonstrated and 100% fractional tuning over 360 range is achieved without changing the shape of the spectral response. The tuning mechanism is based on the use of slow and fast light effects in semiconductor optical amplifiers assisted by optical filtering.
Widely tunable microwave photonic notch filter based on slow and fast light effects
Xue, Weiqi; Sales, Salvador; Mørk, Jesper;
2009-01-01
A continuously tunable microwave photonic notch filter at around 30 GHz is experimentally demonstrated and 100% fractional tuning over 360 range is achieved without changing the shape of the spectral response. The tuning mechanism is based on the use of slow and fast light effects in semiconductor...... optical amplifiers assisted by optical filtering....
A Novel Digitally Tunable Microwave-Photonic Notch Filter Using Differential Group-Delay Module
Yu, Paul K.L.
2003-01-01
We demonstrate a digitally tunable microwave-photonic notch filter based on a differential group-delay module which produces a relative delay between two orthogonal polarizations. A maximum rejection level greater than 60 dB is obtained. The tenability of the filter is achieved through real-time electronic control of the relative delay time.
Si3N4 ring resonator-based microwave photonic notch filter with an ultrahigh peak rejection
Marpaung, David; Morrison, Blair; Pant, Ravi; Roeloffzen, Chris; Leinse, Arne; Hoekman, Marcel; Heideman, Rene; Eggleton, Benjamin J.
2013-01-01
We report a simple technique in microwave photonic (MWP) signal processing that allows the use of an optical filter with a shallow notch to exhibit a microwave notch filter with anomalously high rejection level. We implement this technique using a low-loss, tunable Si3N4 optical ring resonator as the optical filter, and achieved an MWP notch filter with an ultra-high peak rejection > 60 dB, a tunable high resolution bandwidth of 247-840 MHz, and notch frequency tuning of 2-8 GHz. To our knowl...
Li Xiaoyan
2011-11-01
Full Text Available Abstract Background Notch filtering is the most commonly used technique for suppression of power line and harmonic interference that often contaminate surface electromyogram (EMG signals. Notch filters are routinely included in EMG recording instrumentation, and are used very often during clinical recording sessions. The objective of this study was to quantitatively assess the effects of notch filtering on electrically evoked myoelectric signals and on the related motor unit index measurements. Methods The study was primarily based on an experimental comparison of M wave recordings and index estimates of motor unit number and size, with the notch filter function of the EMG machine (Sierra Wave EMG system, Cadwell Lab Inc, Kennewick, WA, USA turned on and off, respectively. The comparison was implemented in the first dorsal interosseous (FDI muscle from the dominant hand of 15 neurologically intact subjects and bilaterally in 15 hemiparetic stroke subjects. Results On average, for intact subjects, the maximum M wave amplitude and the motor unit number index (MUNIX estimate were reduced by approximately 22% and 18%, respectively, with application of the built-in notch filter function in the EMG machine. This trend held true when examining the paretic and contralateral muscles of the stroke subjects. With the notch filter on vs. off, across stroke subjects, we observed a significant decrease in both maximum M wave amplitude and MUNIX values in the paretic muscles, as compared with the contralateral muscles. However, similar reduction ratios were obtained for both maximum M wave amplitude and MUNIX estimate. Across muscles of both intact and stroke subjects, it was observed that notch filtering does not have significant effects on motor unit size index (MUSIX estimate. No significant difference was found in MUSIX values between the paretic and contralateral muscles of the stroke subjects. Conclusions The notch filter function built in the EMG
Notch Filter Analysis and Its Application in Passive Coherent Location Radar (in English
Li Ji-chuan
2015-01-01
Full Text Available The Normalized Least-Mean-Squares (NLMS algorithm is widely used to cancel the direct and multiple path interferences in Passive Coherent Location (PCL radar systems. This study proposes that the interference cancelation using the NLMS algorithm and the calculation of the radar Cross Ambiguity Function (CAF can be modeled as a notch filter, with the notch located at zero Doppler frequency in the surface of the radar CAF. The analysis shows that the notch’s width and depth are closely related to the step size of the NLMS algorithm. Subsequently, the effect of the notch in PCL radar target detection is analyzed. The results suggest that the detection performance of the PCL radar deteriorates because of the wide notch. Furthermore, the Nonuniform NLMS (NNLMS algorithm is proposed for removing the clutter with the Doppler frequency by using notch filtering. A step-size matrix is adopted to mitigate the low Doppler frequency clutter and lower the floor of the radar CAF. With the step-size matrix, can be obtained notches of different depths and widths in different range units of the CAF, which can filter the low Doppler frequency clutter. In addition, the convergence rate of the NNLMS algorithm is better than that of the traditional NLMS algorithm. The validity of the NNLMS algorithm is verified by experimental results.
R K Mehra; P O Arambel; A M Sampath; R K Prasanth; T C Parham
2000-04-01
New algorithms and results are presented for flutter testing and adaptive notching of structural modes in V-22 tiltrotor aircraft based on simulated and flight-test data from Bell Helicopter Textron, Inc. (BHTI). For flutter testing and the identification of structural mode frequencies, dampings and mode shapes, time domain state space techniques based on Deterministic Stochastic Realization Algorithms (DSRA) are used to accurately identify multiple modessimultaneously from sine sweep and other multifrequency data, resulting in great savings over the conventional Prony method. Two different techniques for adaptive notching are explored in order to design an Integrated Flight Structural Control (IFSC) system. The first technique is based on on-line identification of structural mode parameters using DSRA algorithm and tuning of a notch filter. The second technique is based on decoupling rigid-body and structural modes of the aircraft by means of a Kalman filter and using rigid-body estimates in the feedback control loop. The difference between the two approaches is that on-line identification and adaptive notching in the first approach are entirely based on the knowledge of structural modes, whereas the Kalman filter design in the second approach is based on the rigid-body dynamic model only.In the first IFSC design, on-line identification is necessary for flight envelope expansion and to adjust the notch filter frequencies and suppress aero-servoelastic instabilities due to changing flight conditionssuch as gross weight, sling loads, and airspeed. It isshown that by tuning the notch filterfrequency to the identified frequency, the phase lag is reduced and the corresponding structural mode is effectively suppressed and stability is maintained. In the second IFSC design using Kalman filter design, the structural modes are again effectively suppressed. Furthermore, the rigid-body estimates are found to be fairly insensitive to both natural frequency and damping factor
Adaptive filters: stable but divergent
Rupp, Markus
2015-12-01
The pros and cons of a quadratic error measure in the context of various applications have often been discussed. In this tutorial, we argue that it is not only a suboptimal but definitely the wrong choice when describing the stability behavior of adaptive filters. We take a walk through the past and recent history of adaptive filters and present 14 canonical forms of adaptive algorithms and even more variants thereof contrasting their mean-square with their l 2-stability conditions. In particular, in safety critical applications, the convergence in the mean-square sense turns out to provide wrong results, often not leading to stability at all. Only the robustness concept with its l 2-stability conditions ensures the absence of divergence.
Si3N4 ring resonator-based microwave photonic notch filter with an ultrahigh peak rejection
Marpaung, David; Morrison, Blair; Pant, Ravi; Roeloffzen, Chris; Leinse, Arne; Hoekman, Marcel; Heideman, René; Eggleton, Benjamin J.
2013-01-01
We report a simple technique in microwave photonic (MWP) signal processing that allows the use of an optical filter with a shallow notch to exhibit a microwave notch filter with anomalously high rejection level. We implement this technique using a low-loss, tunable Si3N4 optical ring resonator as th
Fibre Optic Notch Filter For The Antiproton Decelerator Stochastic Cooling System
Simmonds, Max Vincent John
2016-01-01
The project scope included reverse engineering, upgrading, and recovering the operational conditions of an existing fibre optic notch filter. Once operational, tests were to be preformed to confirm the performance of the temperature stabilisation. The end goal is to use said notch filter in the Antiproton Decelerator (AD) facility at CERN to help aid antimatter research. The notch filter was successfully reverse engineered and then documented. Changes were made in order to increase performance and reliability, and also allow easy integration into the AD. An additional phase was added whereby the notch filter was to be controller via a touchscreen computer, situated next to the filter, allowing engineers to set-up each of the electronic devices used. While one of the devices (Motorised Delay Line) can be controlled by the touchscreen computer, the other two cannot.Due to time constraints and difficulties with the Beckhoff TwincatII programming language, the USB devices were not able to be controlled via the To...
Periodic Noise Suppression from ECG Signal using Novel Adaptive Filtering Techniques
Yogesh Sharma
2012-03-01
Full Text Available Electrocardiogram signal most commonly known recognized and used biomedical signal for medical examination of heart. The ECG signal is very sensitive in nature, and even if small noise mixed with original signal, the various characteristics of the signal changes, Data corrupted with noise must either filtered or discarded, filtering is important issue for design consideration of real time heart monitoring systems. Various filters used for removing the noise from ECG signals, most commonly used filters are Notch Filters, FIR filters, IIR filters, Wiener filter, Adaptive filters etc. Performance analysis shows that the best result is obtained by using Adaptive filter to remove various noises from ECG signal and get significant SNR andMSE results. In this paper a novel adaptive approach by using LMS algorithm and delay has shown whichcan be used for pre-processing of ECG signal and give appreciable result.
Design of UWB Bandpass Filter with Notched Band Using Distributed CRLH Transmission Lines
Gyuje Sung
2015-08-01
Full Text Available This study presents an Ultra-Wideband (UWB filter with a notched band. The filter adopts novel Composite Right/Left-Handed (CRLH Transmission Lines (TLs, the unit cell of which is theoretically analyzed to derive the design formulas. A model of the CRLH TLs is composed with distributed elements rather than lumped elements. Based on the results of the analysis, it is confirmed that the proposed structures are CRLH TLs. A UWB bandpass filter with a notched band is designed and fabricated using the induced formulas. The measurement results show that the fabricated UWB bandpass filter has an insertion loss of less than 3 dB, a bandwidth of 2.8-10.5 GHz and a rejection of greater than 27 dB at 5.75 GHz.
Thin-film optical notch filter spectacle coatings for the treatment of migraine and photophobia.
Hoggan, Ryan N; Subhash, Amith; Blair, Steve; Digre, Kathleen B; Baggaley, Susan K; Gordon, Jamison; Brennan, K C; Warner, Judith E A; Crum, Alison V; Katz, Bradley J
2016-06-01
Previous evidence suggests optical treatments hold promise for treating migraine and photophobia. We designed an optical notch filter, centered at 480nm to reduce direct stimulation of intrinsically photosensitive retinal ganglion cells. We used thin-film technology to integrate the filter into spectacle lenses. Our objective was to determine if an optical notch filter, designed to attenuate activity of intrinsically photosensitive retinal ganglion cells, could reduce headache impact in chronic migraine subjects. For this randomized, double-masked study, our primary endpoint was the Headache Impact Test (HIT-6; GlaxoSmithKline, Brentford, Middlesex, UK). We developed two filters: the therapeutic filter blocked visible light at 480nm; a 620nm filter was designed as a sham. Participants were asked to wear lenses with one of the filters for 2weeks; after 2weeks when no lenses were worn, they wore lenses with the other filter for 2weeks. Of 48 subjects, 37 completed the study. Wearing either the 480 or 620nm lenses resulted in clinically and statistically significant HIT-6 reductions. However, there was no significant difference when comparing overall effect of the 480 and 620nm lenses. Although the 620nm filter was designed as a sham intervention, research published following the trial indicated that melanopsin, the photopigment in intrinsically photosensitive retinal ganglion cells, is bi-stable. This molecular property may explain the unexpected efficacy of the 620nm filter. These preliminary findings indicate that lenses outfitted with a thin-film optical notch filter may be useful in treating chronic migraine. PMID:26935748
Review Of Parameter Estimation Using Adaptive Filtering
LALITA RANI, SHALOO KIKAN
2013-07-01
Full Text Available In this paper, a comparative study of different adaptive filter algorithm for channel parameter estimation is described. We presented different parameter estimation approaches of adaptive filtering. An extended Kalman filter is then applied as a near-optimal solution to the adaptive channel parameter estimation problem. Kalman filtering is applied for motion parameters resulting in optimal pose estimation. A parallel Kalman filter is applied for joint estimation of code delay, multipath gains and Doppler shift. In this paper, a complete review of parameter estimation using adaptive filtering is explained.
In-plane deeply-etched optical MEMS notch filter with high-speed tunability
Sabry, Yasser M.; Eltagoury, Yomna M.; Shebl, Ahmed; Soliman, Mostafa; Sadek, Mohamed; Khalil, Diaa
2015-12-01
Notch filters are used in spectroscopy, multi-photon microscopy, fluorescence instrumentation, optical sensors and other life science applications. One type of notch filter is based on a fiber-coupled Fabry-Pérot cavity, which is formed by a reflector (external mirror) facing a dielectric-coated end of an optical fiber. Tailoring this kind of optical filter for different applications is possible because the external mirror has fewer mechanical and optical constraints. In this paper we present optical modeling and implementation of a fiber-coupled Fabry-Pérot filter based on dielectric-coated optical fiber inserted into a micromachined fiber groove facing a metallized micromirror, which is driven by a high-speed MEMS actuator. The optical MEMS chip is fabricated using deep reactive ion etching (DRIE) technology on a silicon on insulator wafer, where the optical axis is parallel to the substrate (in-plane) and the optical/mechanical components are self-aligned by the photolithographic process. The DRIE etching depth is 150 μm, chosen to increase the micromirror optical throughput and improving the out-of-plane stiffness of the MEMS actuator. The MEMS actuator type is closing-gap, while its quality factor is almost doubled by slotting the fixed plate. A low-finesse Fabry-Pérot interferometer is formed by the metallized surface of the micromirror and a cleaved end of a standard single-mode fiber, for characterization of the MEMS actuator stroke and resonance frequency. The actuator achieves a travel distance of 800 nm at a resonance frequency of 89.9 kHz. The notch filter characteristics were measured using an optical spectrum analyzer, and the filter exhibits a free spectral range up to 100 nm and a notch rejection ratio up to 20 dB around a wavelength of 1300 nm. The presented device provides batch processing and low-cost production of the filter.
Characteristics of a Tunable Microwave Photonics Notch Filter Based on Two Fiber Bragg Gratings
YUXianbin; ZHANGXianmin; CHIHao; CHENKangsheng
2005-01-01
We investigate theoretically the characteristic of a tunable microwave fiber-optic notch flter based on two fiber gratings. The microwave frequency response based on the refiectivities of two fiber gratings is analyzed and the optimum filter condition is obtained. The refiectivity of the first fiber grating can be tuned experimentally by adjusting the wavelength of input light. Experimental results are in agreement with the theory. The largest notch depth is more than 15dB. The free-spectral range can be tuned by altering the length of fiber between two fiber gratings.
Hong, Xuezhi; Wang, Dawei; Xu, Lei; He, Sailing
2010-06-01
A novel approach is proposed and experimentally demonstrated for optical steganography transmission in WDM networks using temporal phase coded optical signals with spectral notch filtering. A temporal phase coded stealth channel is temporally and spectrally overlaid onto a public WDM channel. Direct detection of the public channel is achieved in the presence of the stealth channel. The interference from the public channel is suppressed by spectral notching before the detection of the optical stealth signal. The approach is shown to have good compatibility and robustness to the existing WDM network for optical steganography transmission. PMID:20588368
Adaptive Filter in SAR Interferometry Derived DEM
XU Caijun; WANG Hua; WANG Jianglin; GE Linlin
2005-01-01
In this paper, the performance of median filter, elevation dependent adaptive sigma median filter, and directionally dependent adaptive sigma median filter are tested on both InSAR Tandem DEM and simulated high-level noisy DEM. Through the comparison, the directionally dependent adaptive sigma median filter is proved to be the most effective one not only in the noise removing but also in the boundary preserve.
A Review of Bandpass with Tunable Notch Microwave Filter in Wideband Application
Anthony Bruster
2015-06-01
Full Text Available In the last few years, several microwave filter design with band-pass response have been proposed for ultra-wideband (UWB application. Among various microwave filter design, microstrip filter are mostly used by researcher due to their low profile, light weight, easy to fabricate and low cost. Conventional microstrip filter can be in any shape whether circular, rectangular or elliptical but some modification or additional variation in their basic design can be made for different purposes for example notch response and tunable characteristic in order to eliminate undesired signal. This paper proposed a compilation of important review about filter design for band-pass filter and discussion about various design with different method or technique used in order to achieve in wideband application range and tunable capabilities. The previous work will be examined and critically analyzed in terms of insertion and return losses, bandwidth, selectivity and tuning in order to proposed novel design of microwave filter with band-pass and tunable notch response in UWB application for future research work. Through this review, we hope that a better understanding of microwave bandpass filter can be established and therefore can have a huge contribution.
Split quaternion nonlinear adaptive filtering.
Ujang, Bukhari Che; Took, Clive Cheong; Mandic, Danilo P
2010-04-01
A split quaternion learning algorithm for the training of nonlinear finite impulse response adaptive filters for the processing of three- and four-dimensional signals is proposed. The derivation takes into account the non-commutativity of the quaternion product, an aspect neglected in the derivation of the existing learning algorithms. It is shown that the additional information taken into account by a rigorous treatment of quaternion algebra provides improved performance on hypercomplex processes. A rigorous analysis of the convergence of the proposed algorithms is also provided. Simulations on both benchmark and real-world signals support the approach. PMID:19926443
Novel Notched UWB Filter Using Stepped Impedance Stub Loaded Microstrip Resonator and Spurlines
Ramkumar Uikey
2015-01-01
Full Text Available This paper presents a novel ultrawideband (UWB bandpass filter using stepped impedance stub loaded microstrip resonator (SISLMR. The proposed resonator is so formed to allow its four resonant frequencies in the UWB passband, which extends from 3.1 GHz to 10.6 GHz. Moreover, two spurline sections are employed to create a sharp notched-band filter for suppressing the signals of 5 GHz WLAN devices. Experimental results of the fabricated filters are in good agreement with the HFSS simulations and validate the design.
Tunable microwave notch filter created by stimulated Brillouin scattering in a silicon chip
Casas-Bedoya, A.; Morrison, Blair; Pagani, Mattia; Marpaung, David; Eggleton, Benjamin J.
2015-12-01
We show the first functional signal processing device based on forward stimulated Brillouin scattering from a silicon nanowire. We harness 1dB of SBS gain to create a high performance, energy efficient microwave photonic notch filter. The filter possess 48dB of suppression, 98 MHz linewidth, and is tunable within a 6 GHz bandwidth. This demonstration represents a significant advance in integrated microwave photonics with potential applications in on-chip microwave signal processing and establish the foundation towards the first CMOS-compatible high performance RF photonic filter.
Single Molecule DNA Detection with an Atomic Vapor Notch Filter
Uhland, Denis; Widmann, Matthias; Lee, Sang-Yun; Wrachtrup, Jörg; Gerhardt, Ilja
2015-01-01
The detection of single molecules has facilitated many advances in life- and material-sciences. Commonly, it founds on the fluorescence detection of single molecules, which are for example attached to the structures under study. For fluorescence microscopy and sensing the crucial parameters are the collection and detection efficiency, such that photons can be discriminated with low background from a labeled sample. Here we show a scheme for filtering the excitation light in the optical detection of single stranded labeled DNA molecules. We use the narrow-band filtering properties of a hot atomic vapor to filter the excitation light from the emitted fluorescence of a single emitter. The choice of atomic sodium allows for the use of fluorescent dyes, which are common in life-science. This scheme enables efficient photon detection, and a statistical analysis proves an enhancement of the optical signal of more than 15% in a confocal and in a wide-field configuration.
Lee, Kwanil; Lee, Ju Han; Lee, Sang Bae
2009-07-20
A novel photonic microwave notch filter with capability of frequency tuning is proposed and experimentally demonstrated. The scheme is based on a fiber Bragg grating (FBG)-based, single longitudinal mode, wavelength-spacing tunable dual-wavelength fiber laser and a dispersive fiber delay line. By using a symmetrical S-bending technique along the FBGs, the wavelength spacing of the laser can be tuned, which enables the microwave notch frequency tuning. Experimental results show that the notch rejection of more than 30 dB and the flexible tunability of notch frequency can be readily achieved in the range of 1.2 approximately 6.7 GHz. PMID:19654727
Fokker-Planck approach to stochastic momentum cooling with a notch filter
A Fokker-Planck equation that describes the stochastic momentum cooling with a notch filter is formulated. The Fokker-Planck coefficients are calculated for an idealized linear notch filter. The coherent energy correction is expressed explicitly with a parameter representing the difference of the particle's time-of-flight and the signal's transmission time from the pickup to the kicker. This formulation provides us a guide to determine the system passband and a setting accuracy of the signal's transmission time for a given momentum spread of the beam. From the Fokker-Planck equation, a differential equation for time variation of a standard deviation of energy error is derived to obtain the initial cooling time and the final momentum spread. This formulation is useful for analysis of experimental data and for design of the cooling system, i.e. optimization of the system passband and gain, specification of accuracy of the signal's transmission time, etc.. (author)
Single molecule DNA detection with an atomic vapor notch filter
Uhland, Denis; Rendler, Torsten; Widmann, Matthias; Lee, Sang-Yun [University of Stuttgart and Stuttgart Research Center of Photonic Engineering (SCoPE) and IQST, 3rd Physics Institute, Stuttgart (Germany); Wrachtrup, Joerg; Gerhardt, Ilja [University of Stuttgart and Stuttgart Research Center of Photonic Engineering (SCoPE) and IQST, 3rd Physics Institute, Stuttgart (Germany); Max Planck Institute for Solid State Research, Stuttgart (Germany)
2015-12-01
The detection of single molecules has facilitated many advances in life- and material-science. Commonly the fluorescence of dye molecules is detected, which are attached to a non-fluorescent structure under study. For fluorescence microscopy one desires to maximize the detection efficiency together with an efficient suppression of undesired laser leakage. Here we present the use of the narrow-band filtering properties of hot atomic sodium vapor to selectively filter the excitation light from the red-shifted fluorescence of dye labeled single-stranded DNA molecules. A statistical analysis proves an enhancement in detection efficiency of more than 15% in a confocal and in a wide-field configuration. (orig.)
Homemade notch filter to suppress strong FM or DAB - T/DVB - T signals
Monstein, Christian
2016-04-01
Many of the current 116 solar radio spectrometer instruments in the e-Callisto network are suffering from strong interference from FM-radio, DAB-T or DVB-T broadcast stations. With simple surface mount device (SMD) components a cheap notch (trap)filter can be produced to suppress these strong signals that otherwise may saturate the low noise amplifier and/or the receiver.
Xue, Weiqi; Sales, Salvador; Mørk, Jesper;
2009-01-01
We introduce a novel scheme based on slow and fast light effects in semiconductor optical amplifiers, to implement a microwave photonic notch filter with ~100% fractional tuning range at a microwave frequency of 30 GHz.......We introduce a novel scheme based on slow and fast light effects in semiconductor optical amplifiers, to implement a microwave photonic notch filter with ~100% fractional tuning range at a microwave frequency of 30 GHz....
A uniformly convergent adaptive particle filter
Papavasiliou, Anastasia
2005-01-01
Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the convergence of this filter to the correct optimal filter, as time and the number...
Adaptive Filtering Algorithms and Practical Implementation
Diniz, Paulo S R
2013-01-01
In the fourth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation. The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes: Analytical and simulation examples in Chapters 4, 5, 6 and 10 Appendix E, which summarizes the analysis of set-membership algorithm Updated problems and references Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more. Several problems are...
Objects tracking with adaptive correlation filters and Kalman filtering
Ontiveros-Gallardo, Sergio E.; Kober, Vitaly
2015-09-01
Object tracking is commonly used for applications such as video surveillance, motion based recognition, and vehicle navigation. In this work, a tracking system using adaptive correlation filters and robust Kalman prediction of target locations is proposed. Tracking is performed by means of multiple object detections in reduced frame areas. A bank of filters is designed from multiple views of a target using synthetic discriminant functions. An adaptive approach is used to improve discrimination capability of the synthesized filters adapting them to multiple types of backgrounds. With the help of computer simulation, the performance of the proposed algorithm is evaluated in terms of detection efficiency and accuracy of object tracking.
Spoof surface plasmon polaritons based notch filter for ultra-wideband microwave waveguide
Xiao, Binggang; Kong, Sheng; Xiao, Sanshui
2016-09-01
Spoof surface plasmon polaritons based notch filter for ultra-wideband microwave waveguide is proposed. Owing to subwavelength confinement, such a filter has advantage in the structure size without sacrificing the performance. The spoof SPP based notch is introduced to suppress the WLAN and satellite communication interference simultaneously. Both the cutoff frequency and the notch frequency are sensitive to the structure parameters, and the cut-off frequency can reach 20 GHz. An adiabatic transition relying on gradient hole-size and flaring ground is designed to effectively couple energy into spoof SPP waveguide. The result shows its cut-off frequency of 17.4 GHz with the insertion loss better than 3 dB during the whole pass-band, while having more than 20 dB rejections at 5.36 GHz and 9.32 GHz with 10 dB fractional bandwidth 1.07% and 0.74% respectively to avoid the existing WLAN and satellite communication signals. Due to planar structures proposed here, it is easy to integrate in the microwave integrated systems, which can play an important role in the microwave communication circuit and system.
Lu, Lei; Song, Young-Yeal; Bevivino, Joshua; Wu, Mingzhong
2010-10-01
There is a critical need for planar millimeter (mm) wave devices. To meet this need, one important strategy is in the use of high-anisotropy hexagonal ferrite films. The high internal anisotropy field for the hexagonal ferrites can be used to realize low-loss devices in the 30-100 GHz regime without the need for high external magnetic fields. Previous work has demonstrated the use of M-type barium hexagonal ferrite (BaM) films and ferromagnetic resonance therein to make mm-wave notch filters. This presentation reports on a new mm-wave notch filter that uses magnetostatic wave (MSW) resonance in BaM films. The device consists of a BaM film strip positioned on the top of a coplanar waveguide (CPW), with the strip's length along the CPW signal line. The BaM strip was grown by pulsed laser deposition and had uniaxial anisotropy along the strip's length. The device showed a band-stop filtering response centered at 53 GHz in absence of external fields. One can increase this frequency with nonzero external fields. A reduction in the strip's width resulted in an enhancement in peak absorption. This filtering response resulted from MSW resonance across the BaM strip's width. The MSW modes were excited by CPW-produced non-uniform alternating magnetic fields.
Adaptive filtering using Higher Order Statistics (HOS
Abdelghani Manseur
2012-03-01
Full Text Available The performed job, in this study, consists in studying adaptive filters and higher order statistics (HOS to ameliorate their performances, by extension of linear case to non linear filters via Volterra series. This study is, principally, axed on: „ Choice of the adaptation step and convergence conditions. „ Convergence rate. „ Adaptive variation of the convergence factor, according to the input signal. The obtained results, with real signals, have shown computationally efficient and numerically stable algorithms for adaptive nonlinear filtering while keeping relatively simple computational complexity.
Adaptive control of active filter using DSP
In order to reduce output-voltage ripple of power supply, an active filter is necessary. In this paper, the active filter with DSP is proposed. The waveform from active filter can be flexibly improved by DSP programming. The output-voltage ripple can be enough reduced by mixing frequency components of the input-voltage ripple. The result of adaptive control using LMS algorism is presented. The improvement by using filtered-X method is discussed. (author)
ADAPTIVE TRILATERAL FILTER FOR IN-LOOP FILTERING
Akitha Kesireddy
2014-07-01
Full Text Available High Efficiency Video Coding (HEVC has achieved significant coding efficiency improvement beyond existing video coding standard by employing several new coding tools. Deblocking Filter, Sample Adaptive Offset (SAO and Adaptive Loop Filter (ALF for in-loop filtering are currently introduced for the HEVC standard. However, these filters are implemented in spatial domain despite the fact of temporal correlation within video sequences. To reduce the artifacts and better align object boundaries in video, a proposed algorithm in in-loop filtering is proposed. The proposed algorithm is implemented in HM-11.0 software. This proposed algorithm allows an average bitrate reduction of about 0.7% and improves the PSNR of the decoded frame by 0.05%, 0.30% and 0.35% in luminance and chroma.
Adaptive filtering in biological signal processing.
Iyer, V K; Ploysongsang, Y; Ramamoorthy, P A
1990-01-01
The high dependence of conventional optimal filtering methods on the a priori knowledge of the signal and noise statistics render them ineffective in dealing with signals whose statistics cannot be predetermined accurately. Adaptive filtering methods offer a better alternative, since the a priori knowledge of statistics is less critical, real time processing is possible, and the computations are less expensive for this approach. Adaptive filtering methods compute the filter coefficients "on-line", converging to the optimal values in the least-mean square (LMS) error sense. Adaptive filtering is therefore apt for dealing with the "unknown" statistics situation and has been applied extensively in areas like communication, speech, radar, sonar, seismology, and biological signal processing and analysis for channel equalization, interference and echo canceling, line enhancement, signal detection, system identification, spectral analysis, beamforming, modeling, control, etc. In this review article adaptive filtering in the context of biological signals is reviewed. An intuitive approach to the underlying theory of adaptive filters and its applicability are presented. Applications of the principles in biological signal processing are discussed in a manner that brings out the key ideas involved. Current and potential future directions in adaptive biological signal processing are also discussed. PMID:2180633
Adaptable Iterative and Recursive Kalman Filter Schemes
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.
Performance Analysis of LMS Adaptive FIR Filter and RLS Adaptive FIR Filter for Noise Cancellation
Jyotsna Yadav
2013-06-01
Full Text Available Interest in adaptive filters continues to grow as they begin to find practical real-time applications in areas such as channel equalization, echo cancellation, noise cancellation and many other adaptive signal processing applications. The key to successful adaptive signal processing understands the fundamental properties of adaptive algorithms such as LMS, RLS etc. Adaptive filter is used for the cancellation of the noise component which is overlap with undesired signal in the same frequency range. This paper presents design, implementation and performance comparison of adaptive FIR filter using LMS and RMS algorithms. MATLAB Simulink environment are used for simulations.
Performance Analysis of LMS Adaptive FIR Filter and RLS Adaptive FIR Filter for Noise Cancellation
Jyotsna Yadav
2013-07-01
Full Text Available Interest in adaptive filters continues to grow as they begin to find practical real-time applications in areassuch as channel equalization, echo cancellation, noise cancellation and many other adaptive signalprocessing applications. The key to successful adaptive signal processing understands the fundamentalproperties of adaptive algorithms such as LMS, RLS etc. Adaptive filter is used for the cancellation of thenoise component which is overlap with undesired signal in the same frequency range. This paper presentsdesign, implementation and performance comparison of adaptive FIR filter using LMS and RMSalgorithms. MATLAB Simulink environment are used for simulations.
Recursive total-least-squares adaptive filtering
Dowling, Eric M.; DeGroat, Ronald D.
1991-12-01
In this paper a recursive total least squares (RTLS) adaptive filter is introduced and studied. The TLS approach is more appropriate and provides more accurate results than the LS approach when there is error on both sides of the adaptive filter equation; for example, linear prediction, AR modeling, and direction finding. The RTLS filter weights are updated in time O(mr) where m is the filter order and r is the dimension of the tracked subspace. In conventional adaptive filtering problems, r equals 1, so that updates can be performed with complexity O(m). The updates are performed by tracking an orthonormal basis for the smaller of the signal or noise subspaces using a computationally efficient subspace tracking algorithm. The filter is shown to outperform both LMS and RLS in terms of tracking and steady state tap weight error norms. It is also more versatile in that it can adapt its weight in the absence of persistent excitation, i.e., when the input data correlation matrix is near rank deficient. Through simulation, the convergence and tracking properties of the filter are presented and compared with LMS and RLS.
Pena-Alzola, Rafael; Liserre, Marco; Blaabjerg, Frede;
2014-01-01
LCL-filters are a cost-effective solution to mitigate harmonic current content in grid-tie converters. In order to avoid stability problems, the resonance frequency of LCL-filters can be damped with active techniques that remove dissipative elements but increase control complexity. A notch filter...... impedance variations due to its ability to tune the grid-tie inverter on-site. Finally, the analysis is validated with both simulation and experiments....
Polyphase Analysis of Subband Adaptive Filters
Weiss, S; Stewart, R W; Harteneck, M; Stenger, A.
1999-01-01
In this paper, we derive a polyphase analysis to determine the optimum filters in a subband adaptive filter (SAF) system. The structure of this optimum solution deviates from the standard SAF approach and presents its best possible solution only as an approximation. Besides this new insight into SAF error sources, the discussed analysis allows to calculate the optimum subband responses and the standard SAF approximation. Examples demonstrating the validity of our analysis and its use for dete...
Medhin, Ashenafi Kiros; Kamchevska, Valerija; Galili, Michael; Oxenløwe, Leif Katsuo
We experimentally perform 1×4 optical packet switching of variable length 640 Gbit/s OTDM data packets using in-band notch-filter labeling with only 2.7-dB penalty. Up to 8 notches are employed to demonstrate scalability of the labeling scheme to 1×256 switching operation.......We experimentally perform 1×4 optical packet switching of variable length 640 Gbit/s OTDM data packets using in-band notch-filter labeling with only 2.7-dB penalty. Up to 8 notches are employed to demonstrate scalability of the labeling scheme to 1×256 switching operation....
Adaptive Filters for Muscle Response Suppression
Sennels, Søren; Biering-Soerensen, Fin; Hansen, Steffen Duus;
1996-01-01
are proposed, based on the observation that the shape of the muscle responses only exhibits moderate changes during a time window of up to 300 ms. The filters are derived and compared with a conventional fixed comb filter on both simulated and real data. For variations in amplitude of the muscle responses......To be able to use the voluntary EMG-signal from an electrically stimulated muscle as control signal for FES-applications, it is necessary to eliminate the muscle response evoked by the stimulation. The muscle response is a non-stationary signal, therefore a set of linear adaptive prediction filters...
Partial update least-square adaptive filtering
Xie, Bei
2014-01-01
Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity (O(N)) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster a
Sparse adaptive filters for echo cancellation
Paleologu, Constantin
2011-01-01
Adaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellati
Nonlinear Adaptive Filters based on Particle Swarm Optimization
Faten BEN ARFIA; Ben Messaoud, Mohamed; Abid, Mohamed
2009-01-01
This paper presents a particle swarm optimization (PSO) algorithm to adjust the parameters of the nonlinear filter and to make this type of the filters more powerful for the elimination of the Gaussian noise and also the impulse noise. In this paper we apply the particle swarm optimization to the rational filters and we completed this work with the comparison between our results and other adaptive nonlinear filters like the LMS adaptive median filters and the no-adaptive rational filter.
A Rapid Introduction to Adaptive Filtering
Vega, Leonardo Rey
2013-01-01
In this book, the authors provide insights into the basics of adaptive filtering, which are particularly useful for students taking their first steps into this field. They start by studying the problem of minimum mean-square-error filtering, i.e., Wiener filtering. Then, they analyze iterative methods for solving the optimization problem, e.g., the Method of Steepest Descent. By proposing stochastic approximations, several basic adaptive algorithms are derived, including Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS) and Sign-error algorithms. The authors provide a general framework to study the stability and steady-state performance of these algorithms. The affine Projection Algorithm (APA) which provides faster convergence at the expense of computational complexity (although fast implementations can be used) is also presented. In addition, the Least Squares (LS) method and its recursive version (RLS), including fast implementations are discussed. The book closes with the discussion of severa...
VSP wave separation by adaptive masking filters
Rao, Ying; Wang, Yanghua
2016-06-01
In vertical seismic profiling (VSP) data processing, the first step might be to separate the down-going wavefield from the up-going wavefield. When using a masking filter for VSP wave separation, there are difficulties associated with two termination ends of the up-going waves. A critical challenge is how the masking filter can restore the energy tails, the edge effect associated with these terminations uniquely exist in VSP data. An effective strategy is to implement masking filters in both τ-p and f-k domain sequentially. Meanwhile it uses a median filter, producing a clean but smooth version of the down-going wavefield, used as a reference data set for designing the masking filter. The masking filter is implemented adaptively and iteratively, gradually restoring the energy tails cut-out by any surgical mute. While the τ-p and the f-k domain masking filters target different depth ranges of VSP, this combination strategy can accurately perform in wave separation from field VSP data.
Kernel adaptive filtering a comprehensive introduction
Liu, Weifeng; Haykin, Simon
2010-01-01
Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, O
Filtering Algebraic Multigrid and Adaptive Strategies
Nagel, A; Falgout, R D; Wittum, G
2006-01-31
Solving linear systems arising from systems of partial differential equations, multigrid and multilevel methods have proven optimal complexity and efficiency properties. Due to shortcomings of geometric approaches, algebraic multigrid methods have been developed. One example is the filtering algebraic multigrid method introduced by C. Wagner. This paper proposes a variant of Wagner's method with substantially improved robustness properties. The method is used in an adaptive, self-correcting framework and tested numerically.
Adaptive Importance Sampling in Particle Filtering
Šmídl, Václav; Hofman, Radek
Istanbul : ISIF, 2013. ISBN 978-605-86311-1-3. [16th International Conference on Information Fusion. Istanbul (TR), 09.07.2013-12.07.2013] R&D Projects: GA MV VG20102013018; GA ČR(CZ) GAP102/11/0437 Keywords : importance sampling * sequential monte carlo * sufficient statistics Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2013/AS/smidl-adaptive importance sampling in particle filtering.pdf
Zhang, Guiju; Cao, Bing; Zhang, Ke; Wang, Chinhua; Sun, Qian
2015-01-01
Developments in micro and nanofabrication technologies have led a variety of grating waveguide structures (GWS) being proposed and implemented in optics and laser application systems. A new design of multilayered nanostructure double-grating is described for reflection notch filter. Thin metal film and dielectric film are used and designed with one-dimensional composite gratings. The results calculated by rigorous coupled-wave analysis (RCWA) present that the thin metal film between substrate...
Musical noise reduction using an adaptive filter
Hanada, Takeshi; Murakami, Takahiro; Ishida, Yoshihisa; Hoya, Tetsuya
2003-10-01
This paper presents a method for reducing a particular noise (musical noise). The musical noise is artificially produced by Spectral Subtraction (SS), which is one of the most conventional methods for speech enhancement. The musical noise is the tin-like sound and annoying in human auditory. We know that the duration of the musical noise is considerably short in comparison with that of speech, and that the frequency components of the musical noise are random and isolated. In the ordinary SS-based methods, the musical noise is removed by the post-processing. However, the output of the ordinary post-processing is delayed since the post-processing uses the succeeding frames. In order to improve this problem, we propose a novel method using an adaptive filter. In the proposed system, the observed noisy signal is used as the input signal to the adaptive filter and the output of SS is used as the reference signal. In this paper we exploit the normalized LMS (Least Mean Square) algorithm for the adaptive filter. Simulation results show that the proposed method has improved the intelligibility of the enhanced speech in comparison with the conventional method.
Adaptive noise Wiener filter for scanning electron microscope imaging system.
Sim, K S; Teh, V; Nia, M E
2016-03-01
Noise on scanning electron microscope (SEM) images is studied. Gaussian noise is the most common type of noise in SEM image. We developed a new noise reduction filter based on the Wiener filter. We compared the performance of this new filter namely adaptive noise Wiener (ANW) filter, with four common existing filters as well as average filter, median filter, Gaussian smoothing filter and the Wiener filter. Based on the experiments results the proposed new filter has better performance on different noise variance comparing to the other existing noise removal filters in the experiments. SCANNING 38:148-163, 2016. © 2015 Wiley Periodicals, Inc. PMID:26235517
We propose a novel notch-filtering scheme for bit-rate transparent all-optical NRZ-to-PRZ format conversion. The scheme is based on a two-degree-of-freedom optimally designed fiber Bragg grating. It is shown that a notch filter optimized for any specific operating bit rate can be used to realize high-Q-factor format conversion over a wide bit rate range without requiring any tuning. (paper)
Design of UWB Filter with Notch Band for WLAN (5.3-5.8 GHz Signal Interference Rejection
Vinay Kumar Sharma
2014-10-01
Full Text Available In this letter, a design of Compact Ultra-Wideband (UWB bandpass filter with a switchable notch band for WLAN (5.3-5.9GHz interference rejection is proposed. As 5.3-5.9GHz wireless local area network (WLAN is existed in UWB spectrum range (3.1 -10.6GHz and may interfere with UWB system operation. The UWB bandpass filter is implemented using a basic multiple mode resonator (MMR structure feed by interdigital coupled lines for achieving higher degree of coupling. The notch band is obtained using a etched slot on main microstrip line. The centre frequency and bandwidth of notch band is optimized. The filter is compact in size with dimension 37.4 X 25 mm2 .The electromagnetic simulation software, Computer Simulation Technology Microwave Studio (CST MWS is used for the simulation and analysis of the designed structure. For fabrication of this structure Rogers RT5880 substrate of thickness 0.4 mm and dielectric constant 2.2 is used. Measured and simulated results show good agreement.
Adaptive Filtering Queueing for Improving Fairness
Jui-Pin Yang
2015-06-01
Full Text Available In this paper, we propose a scalable and efficient Active Queue Management (AQM scheme to provide fair bandwidth sharing when traffic is congested dubbed Adaptive Filtering Queueing (AFQ. First, AFQ identifies the filtering level of an arriving packet by comparing it with a flow label selected at random from the first level to an estimated level in the filtering level table. Based on the accepted traffic estimation and the previous fair filtering level, AFQ updates the fair filtering level. Next, AFQ uses a simple packet-dropping algorithm to determine whether arriving packets are accepted or discarded. To enhance AFQ’s feasibility in high-speed networks, we propose a two-layer mapping mechanism to effectively simplify the packet comparison operations. Simulation results demonstrate that AFQ achieves optimal fairness when compared with Rotating Preference Queues (RPQ, Core-Stateless Fair Queueing (CSFQ, CHOose and Keep for responsive flows, CHOose and Kill for unresponsive flows (CHOKe and First-In First-Out (FIFO schemes under a variety of traffic conditions.
An Efficient Adaptive Filtering for CFA Demosaicking
Dev. R. Newlin,
2010-07-01
Full Text Available Most digital still cameras acquire imagery with a color filter array (CFA, sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the highfrequency information of imagery as much as possible, since such information is essential for image visual quality. We discuss in this paper two key observations for preserving high-frequency information in CFA demosaicking: 1 the high frequencies are similar across three color components, and 2 the high frequencies along the horizontal and vertical axes are essential for image quality. Our frequency analysis of CFA samplesindicates that filtering a CFA image can better preserve highfrequencies than filtering each color component separately. Thismotivates us to design an efficient filter for estimating theluminance at green pixels of the CFA image and devise an adaptive filtering approach to estimate the luminance at red and blue pixels. Experimental results on simulated images, as well as raw data, verify that the proposed method outperforms the existing methods both visually and in terms of peak signal-tonoise ratio, at a notably lower computational cost.
Quaternion-valued nonlinear adaptive filtering.
Ujang, Bukhari Che; Took, Clive Cheong; Mandic, Danilo P
2011-08-01
A class of nonlinear quaternion-valued adaptive filtering algorithms is proposed based on locally analytic nonlinear activation functions. To circumvent the stringent standard analyticity conditions which are prohibitive to the development of nonlinear adaptive quaternion-valued estimation models, we use the fact that stochastic gradient learning algorithms require only local analyticity at the operating point in the estimation space. It is shown that the quaternion-valued exponential function is locally analytic, and, since local analyticity extends to polynomials, products, and ratios, we show that a class of transcendental nonlinear functions can serve as activation functions in nonlinear and neural adaptive models. This provides a unifying framework for the derivation of gradient-based learning algorithms in the quaternion domain, and the derived algorithms are shown to have the same generic form as their real- and complex-valued counterparts. To make such models second-order optimal for the generality of quaternion signals (both circular and noncircular), we use recent developments in augmented quaternion statistics to introduce widely linear versions of the proposed nonlinear adaptive quaternion valued filters. This allows full exploitation of second-order information in the data, contained both in the covariance and pseudocovariances to cater rigorously for second-order noncircularity (improperness), and the corresponding power mismatch in the signal components. Simulations over a range of circular and noncircular synthetic processes and a real world 3-D noncircular wind signal support the approach. PMID:21712159
Strong tracking adaptive Kalman filters for underwater vehicle dead reckoning
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.
Mirmohamadsadeghi, Leila; Vesin, Jean-Marc
2016-09-01
Measuring the instantaneous frequency of a signal rapidly and accurately is essential in many applications. However, the instantaneous frequency by definition is a parameter difficult to determine. Fourier-based methods introduce estimation delays as computations are performed in a time-window. Instantaneous methods based on the Hilbert transform lack robustness. State-of-the-art adaptive filters yield accurate estimates, however, with an adaptation delay. In this study we propose an algorithm based on short length-3 FIR notch filters to estimate the instantaneous frequency of a signal at each sample, in a real-time manner and with very low delay. The output powers of a bank of the above-mentioned filters are used in a recursive weighting scheme to estimate the dominant frequency of the input. This scheme has been extended to process multiple inputs containing a common frequency by introducing an additional weighting scheme upon the inputs. The algorithm was tested on synthetic data and then evaluated on real biomedical data, i.e. the estimation of the respiratory rate from the electrocardiogram. It was shown that the proposed method provided more accurate estimates with less delay than those of state-of-the-art methods. By virtue of its simplicity and good performance, the proposed method is a worthy candidate to be used in biomedical applications, for example in health monitoring developments based on portable and automatic devices. PMID:27510318
Nonlinear Adaptive Filters based on Particle Swarm Optimization
Faten BEN ARFIA
2009-07-01
Full Text Available This paper presents a particle swarm optimization (PSO algorithm to adjust the parameters of the nonlinear filter and to make this type of the filters more powerful for the elimination of the Gaussian noise and also the impulse noise. In this paper we apply the particle swarm optimization to the rational filters and we completed this work with the comparison between our results and other adaptive nonlinear filters like the LMS adaptive median filters and the no-adaptive rational filter.
Adaptive ship autopilot with wave filter
Steinar Sælid
1983-01-01
Full Text Available This paper is concerned with analysis and design of an adaptive autopilot for ships. The design is based on a low and high frequency model of the vessel motion adequate to ship steering. The low frequency model describes the vessel response to rudder control and slowly varying environmental forces. The high frequency model represents the wave induced oscillatory part of the yaw motion. The models are used in a Kalman filter and the rudder control is computed from linear quadratic theory based on the low frequency part of the vector. This yields a very effective filtering of the wave component of the yaw motion. Proper operation of this filter/controller structure requires knowledge of the vessel model parameters and the dominating wave frequency. The vessel parameters are estimated on line by a recursive prediction error method. In order to reduce the computing requirements, the state estimator is operated using scheduled gains. This results in an easy and robust design. The convergence properties are investigated by using the method of Ljung. The performance is confirmed by simulation experiments.
Survey of Sparse Adaptive Filters for Acoustic Echo Cancellation
Krishna Samalla
2013-01-01
Full Text Available This paper reviews the existing developments of adaptive methods of sparse adaptive filters for the identification of sparse impulse response in both network and acoustic echo cancellation from the last decade. A variety of different architectures and novel training algorithms have been proposed in literature. At present most of the work in echo cancellation on using more than one method. Sparse adaptive filters take the advantage of each method and showing good improvement in the sparseness measure performance. This survey gives an overview of existing sparse adaptive filters mechanisms and discusses their advantages over the traditional adaptive filters developed for echo cancellation.
Performance Evaluation of Adaptive Filters Structures for Acoustic Echo Cancellation
Sanjeev Dhull
2011-05-01
Full Text Available We have designed and simulated an acoustic echo cancellation system for conferencing. This system is based upon a least-mean-square (LMS adaptive algorithm and uses multi filter technique. A comparative study of both structure has been carried out and it is found that this new multi-filter converge faster than similar single long adaptive filter. Index Terms: LMS,Multiple sub filter ,Echo cancellation
Adaptive Local Image Registration: Analysis on Filter Size
Vishnukumar S; M.Wilscy
2012-01-01
Adaptive Local Image Registration is a Local Image Registration based on an Adaptive Filtering frame work. A filter of appropriate size convolves with reference image and gives the pixel values corresponding to the distorted image and the filter is updated in each stage of the convolution. When the filter converges to the system model, it provides the registered image. The filter size plays an important role in this method. The analysis on the filter size is done using Peak Signal-to-Noise Ra...
Adaptive information filtering for dynamic recommender systems
Jin, Ci-Hang; Zhang, Yi-Cheng; Zhou, Tao
2009-01-01
The dynamic environment in the real world calls for the adaptive techniques for information filtering, namely to provide real-time responses to the changes of system data. Where many incremental algorithms are designed for this purpose, they are usually challenged by the worse and worse performance resulted from the cumulative errors over time. In this Letter, we propose two incremental diffusion-based algorithms for the personalized recommendations, which integrate some pieces of local and fast updatings to achieve the approximate results. In addition to the fast responses, the errors of the proposed algorithms do not cumulate over time, that is to say, the global recomputing is unnecessary. This remarkable advantage is demonstrated by several metrics on algorithmic accuracy for two movie recommender systems and a social bookmarking system.
Modified Adaptive Weighted Averaging Filtering Algorithm for Noisy Image Sequences
LI Weifeng; YU Daoyin; CHEN Xiaodong
2007-01-01
In order to avoid the influence of noise variance on the filtering performances, a modified adaptive weighted averaging (MAWA) filtering algorithm is proposed for noisy image sequences. Based upon adaptive weighted averaging pixel values in consecutive frames, this algorithm achieves the filtering goal by assigning smaller weights to the pixels with inappropriate estimated motion trajectory for noise. It only utilizes the intensity of pixels to suppress noise and accordingly is independent of noise variance. To evaluate the performance of the proposed filtering algorithm, its mean square error and percentage of preserved edge points were compared with those of traditional adaptive weighted averaging and non-adaptive mean filtering algorithms under different noise variances. Relevant results show that the MAWA filtering algorithm can preserve image structures and edges under motion after attenuating noise, and thus may be used in image sequence filtering.
Adaptive Federal Kalman Filtering for SINS/GPS Integrated System
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.
A new adaptive filtering algorithm for systems with multiplicative noise
WANG Hui-li; CHEN Xi-xin; LU Qian-hao
2005-01-01
Presented here is a new adaptive state filtering algorithm for systems with multiplicative noise. This algorithm estimates the vector state of the system and the statistics of noise when all the statistics of noise are unknown. This filtering algorithm is a simple recursive structure. A simulation example is presented which demonstrates the effectiveness of this filtering algorithm.
Performance Evaluation of Adaptive Filters Structures for Acoustic Echo Cancellation
Sanjeev Dhull
2011-05-01
Full Text Available We have designed and simulated an acoustic echo cancellation system for conferencing. Thissystem is based upon a least-mean-square (LMS adaptive algorithm and uses multi filtertechnique. A comparative study of both structure has been carried out and it is found that thisnew multi-filter converge faster than similar single long adaptive filter.
Scheme of adaptive polarization filtering based on Kalman model
Song Lizhong; Qi Haiming; Qiao Xiaolin; Meng Xiande
2006-01-01
A new kind of adaptive polarization filtering algorithm in order to suppress the angle cheating interference for the active guidance radar is presented. The polarization characteristic of the interference is dynamically tracked by using Kalman estimator under variable environments with time. The polarization filter parameters are designed according to the polarization characteristic of the interference, and the polarization filtering is finished in the target cell. The system scheme of adaptive polarization filter is studied and the tracking performance of polarization filter and improvement of angle measurement precision are simulated. The research results demonstrate this technology can effectively suppress the angle cheating interference in guidance radar and is feasible in engineering.
Autonomous Navigation System Using a Fuzzy Adaptive Nonlinear H∞ Filter
Fariz Outamazirt
2014-09-01
Full Text Available Although nonlinear H∞ (NH∞ filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞ filter is proposed for the Unmanned Aerial Vehicle (UAV localization problem. Based on a real-time Fuzzy Inference System (FIS, the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter.
A hybrid RNS adaptive filter for channel equalization
Bernocchi, Gian Luca; Cardarilli, Gian Carlo; Re, Andrea Del;
2006-01-01
In this work a hybrid Residue Number System (RNS) implementation of an adaptive FIR filter is presented. The used adaptation algorithm is the Least Mean Squares (LMS). The filter has been designed to meet the constraints of specific class of applications. In fact, it is suitable for applications...... requiring a large number of taps where a serial updating of the filter coefficients is feasible (channel equalization or echo cancellation). In the literature, it has been shown that the RNS implementation of FIR filters grants earnings in area ad power consumption due to the introduced arithmetic...... simplifications. Vice versa, the RNS implementation of the adaptation algorithm needs scaling circuits that are complex and expensive in RNS arithmetic. For this reason, a serial binary implementation of the adaptation algorithm is chosen. The advantages in terms of area and speed of the RNS adaptive filter with...
Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion
Zongze Wu
2015-10-01
Full Text Available The maximum correntropy criterion (MCC has recently been successfully applied to adaptive filtering. Adaptive algorithms under MCC show strong robustness against large outliers. In this work, we apply the MCC criterion to develop a robust Hammerstein adaptive filter. Compared with the traditional Hammerstein adaptive filters, which are usually derived based on the well-known mean square error (MSE criterion, the proposed algorithm can achieve better convergence performance especially in the presence of impulsive non-Gaussian (e.g., α-stable noises. Additionally, some theoretical results concerning the convergence behavior are also obtained. Simulation examples are presented to confirm the superior performance of the new algorithm.
Sub-band Implementation of Adaptive Nonlinear Filter for Adaptive Nonlinear Echo Cancellation
Dayong Zhou
2007-04-01
Full Text Available The adaptive Volterra filter has been successfully applied in nonlinear acoustic echo cancellation (AEC systems and nonlinear line echo cancellation systems, but its applications are limited by its required computational complexity and slow convergence rate, especially for systems with long memory length. In this paper, we first apply a more general nonlinear filter, the function expansion nonlinear filter, in the acoustic echo cancellation - the Volterra filter can be regarded as special case of the function expansion nonlinear filter. Then by leveraging to a multi-channel configuration of the function expansion nonlinear filter and the sampling theory for nonlinear systems, we extend linear sub-band delay-less adaptive filter techniques to develop an efficient sub-band implementation of the adaptive function expansion nonlinear filter. The developed sub-band configuration of the adaptive nonlinear filter can greatly improve the convergence rate and reduce the computational complexity of nonlinear echo cancellers, which is shown by analyses and simulations.
Spoof surface plasmon polaritons based notch filter for ultra-wideband microwave waveguide
Xiao, Binggang; Li, Sheng-Hua; Xiao, Sanshui
2016-01-01
satellite communication interference simultaneously. Both the cutoff frequency and the notch frequency are sensitive to the structure parameters, and the cut-off frequency can reach 20 GHz. An adiabatic transition relying on gradient hole-size and flaring ground is designed to effectively couple energy into...... satellite communication signals. Due to planar structures proposed here, it is easy to integrate in the microwave integrated systems, which can play an important role in the microwave communication circuit and system....
Adaptive Lattice Filters for CDMA Overlay
Prahatheesan, V; Wang, J.
2000-01-01
This paper presents the behavior of reflection coefficients of a stochastic gradient lattice (SGL) filter applied to a code-division multiple-access overlay system. Analytic expressions for coefficients for a two-stage filter are derived in a Rayleigh fading channel with the presence of narrow-band interference and additive white Gaussian noise. It is shown that the coefficients of the lattice filter exhibit separate tracking and convergent properties,and that compared to an LMS filter, the l...
Guiju ZHANG
2015-11-01
Full Text Available Developments in micro and nanofabrication technologies have led a variety of grating waveguide structures (GWS being proposed and implemented in optics and laser application systems. A new design of multilayered nanostructure double-grating is described for reflection notch filter. Thin metal film and dielectric film are used and designed with one-dimensional composite gratings. The results calculated by rigorous coupled-wave analysis (RCWA present that the thin metal film between substrate and grating can produce significant attenuated reflections and efficiency in a broad reflected spectral range. The behavior of such a reflection filter is evaluated for refractive index sensing, which can be applied inside the integrated waveguide structure while succeeding cycles in measurement. The filter peaks are designed and obtained in a visible range with full width half maximum (FWHM of several nanometers to less than one nanometer. The multilayered structure shows a sensitivity of refractive index of 220nm/RIU as changing the surroundings. The reflection spectra are studied under different periods, depths and duty cycles. The passive structure and its characteristics can achieve practical applications in various fields, such as optical sensing, color filtering, Raman spectroscopy and laser technology.DOI: http://dx.doi.org/10.5755/j01.ms.21.4.9625
A Review of Bandpass with Tunable Notch Microwave Filter in Wideband Application
Anthony Bruster; Zahriladha Zakaria; Eliyana Ruslan; Ariffin Mutalib
2015-01-01
In the last few years, several microwave filter design with band-pass response have been proposed for ultra-wideband (UWB) application. Among various microwave filter design, microstrip filter are mostly used by researcher due to their low profile, light weight, easy to fabricate and low cost. Conventional microstrip filter can be in any shape whether circular, rectangular or elliptical but some modification or additional variation in their basic design can be made for different purposes f...
Adaptive Threshold Median Filter for Multiple-Impulse Noise
JIANG Bo; HUANG Wei
2007-01-01
Attenuating the noises plays an essential role in the image processing. Almost all the traditional median filters concern the removal of impulse noise having a single layer, whose noise gray level value is constant. In this paper, a new adaptive median filter is proposed to handle those images corrupted not only by single layer noise. The adaptive threshold median filter(ATMF) has been developed by combining the adaptive median filter (AMF) and two dynamic thresholds. Because of the dynamic threshold being used, the ATMF is able to balance the removal of the multiple-impulse noise and the quality of image. Comparison of the proposed method with traditional median filters is provided. Some visual examples are given to demonstrate the performance of the proposed Filter.
Low-power adaptive filter based on RNS components
Bernocchi, Gian Luca; Cardarilli, Gian Carlo; Del Re, Andrea;
2007-01-01
least mean squares (LMS) algorithm, is allowed. Previous work showed that the use of the residue number system (RNS) for the variable FIR filter grants advantages both in area and power consumption. On the other hand, the use of a binary serial implementation of the adaptation algorithm eliminates the......In this paper a low-power implementation of an adaptive FIR filter is presented. The filter is designed to meet the constraints of channel equalization for fixed wireless communications that typically requires a large number of taps, but a serial updating of the filter coefficients, based on the...
Decentralized, Adaptive, Look-Ahead Particle Filtering
Ahmed, Mohamed Osama; Bibalan, Pouyan T.; De Freitas, Nando; Fauvel, Simon
2012-01-01
The decentralized particle filter (DPF) was proposed recently to increase the level of parallelism of particle filtering. Given a decomposition of the state space into two nested sets of variables, the DPF uses a particle filter to sample the first set and then conditions on this sample to generate a set of samples for the second set of variables. The DPF can be understood as a variant of the popular Rao-Blackwellized particle filter (RBPF), where the second step is carried out using Monte Ca...
Superresolution restoration of an image sequence: adaptive filtering approach.
Elad, M; Feuer, A
1999-01-01
This paper presents a new method based on adaptive filtering theory for superresolution restoration of continuous image sequences. The proposed methodology suggests least squares (LS) estimators which adapt in time, based on adaptive filters, least mean squares (LMS) or recursive least squares (RLS). The adaptation enables the treatment of linear space and time-variant blurring and arbitrary motion, both of them assumed known. The proposed new approach is shown to be of relatively low computational requirements. Simulations demonstrating the superresolution restoration algorithms are presented. PMID:18262881
Nonlinear Adaptive Filter for MEMS Gyro Error Cancellation Project
National Aeronautics and Space Administration — The Nonlinear adaptive filters (NAF) can learn deterministic gyro errors and cancel the error’s effect from attitude estimates. By completely canceling...
Adaptive Control of Flexible Structures Using Residual Mode Filters
Balas, Mark J.; Frost, Susan
2010-01-01
Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter. We have proposed a modified adaptive controller with a residual mode filter. The RMF is used to accommodate troublesome modes in the system that might otherwise inhibit the adaptive controller, in particular the ASPR condition. This new theory accounts for leakage of the disturbance term into the Q modes. A simple three-mode example shows that the RMF can restore stability to an otherwise unstable adaptively controlled system. This is done without modifying the adaptive controller design.
RLS Adaptive Filtering Algorithms Based on Parallel Computations
V. I. Djigan
2005-01-01
The paper presents a family of the sliding window RLS adaptive filtering algorithms with the regularization of adaptive filter correlation matrix. The algorithms are developed in forms, fitted to the implementation by means of parallel computations. The family includes RLS and fast RLS algorithms based on generalized matrix inversion lemma, fast RLS algorithms based on square root free inverse QR decomposition and linearly constrained RLS algorithms. The considered algorithms are mathematical...
Adaptive Filters Revisited - RFI Mitigation in pulsar observations
Kesteven, M.; Hobbs, G.; Clement, R.; Dawson, B.; Manchester, R.; Uppal, T.
2004-01-01
Pulsar detection and timing experiments are applications where adaptive filters seem eminently suitable tools for radio-frequency-interference (RFI) mitigation. We describe a novel variant which works well in field trials of pulsar observations centred on an observing frequency of 675 MHz, a bandwidth of 64 MHz and with 2-bit sampling. Adaptive filters have generally received bad press for RFI mitigation in radio astronomical observations with their most serious drawback being a spectral echo...
Reducing Background Noise Through a Stethoscope Cup Using Adaptive Filters
Hill, Bryce E.; Christensen, Douglas
2010-01-01
An adaptive filtering algorithm was used to test the validity of adaptively filtering respiratory signals recorded at the trachea with an external reference microphone. Two different setups were tested. The first used a microphone in open air, the second used a microphone that was housed inside a second stethoscope cup. The primary microphone was affixed to a phantom material. External sounds and music were played via aloud speaker to record additive noise data from within the stethoscope cup...
Decentralized, Adaptive, Look-Ahead Particle Filtering
Ahmed, Mohamed Osama; de Freitas, Nando; Fauvel, Simon
2012-01-01
The decentralized particle filter (DPF) was proposed recently to increase the level of parallelism of particle filtering. Given a decomposition of the state space into two nested sets of variables, the DPF uses a particle filter to sample the first set and then conditions on this sample to generate a set of samples for the second set of variables. The DPF can be understood as a variant of the popular Rao-Blackwellized particle filter (RBPF), where the second step is carried out using Monte Carlo approximations instead of analytical inference. As a result, the range of applications of the DPF is broader than the one for the RBPF. In this paper, we improve the DPF in two ways. First, we derive a Monte Carlo approximation of the optimal proposal distribution and, consequently, design and implement a more efficient look-ahead DPF. Although the decentralized filters were initially designed to capitalize on parallel implementation, we show that the look-ahead DPF can outperform the standard particle filter even on ...
Reduction of Radiographic Quantum Noise Using Adaptive Weighted Median Filter
Images are easily corrupted by noise during the data transmission, data capture and data processing. A technical method of noise analyzing and adaptive filtering for reducing of quantum noise in radiography is presented. By adjusting the characteristics of the filter according to local statistics around each pixel of the image as moving windowing, it is possible to suppress noise sufficiently while preserve edge and other significant information required in reading. We have proposed adaptive weighted median(AWM) filters based on local statistics. We show two ways of realizing the AWM filters. One is a simple type of AWM filter, whose weights are given by a simple non-linear function of three local characteristics. The other is the AWM filter which is constructed by homogeneous factor(HF). Homogeneous factor(HF) from the quantum noise models that enables the filter to recognize the local structures of the image is introduced, and an algorithm for determining the HF fitted to the detection systems with various inner statistical properties is proposed. We show by the experimented that the performances of proposed method is superior to these of other filters and models in preserving small details and suppressing the noise at homogeneous region. The proposed algorithms were implemented by visual C++ language on a IBM-PC Pentium 550 for testing purposes, the effects and results of the noise filtering were proposed by comparing with images of the other existing filtering methods
FPGA Implementation of Adaptive Filter and its Performance Analysis
J. Malarmannan
2013-06-01
Full Text Available Adaptive filters are used in various real-time applications such as echo cancellation, noise cancellation, system identification and prediction. Field -programmable gate arrays (FPGAs are alsoused most widely for applications where timing requirements are strict. Thus implementation of filter in real-time is needed. The objective of this paper is to design and implement an Adaptive filter which is robust to impulsive noise using hardware description language (HDL design. The design implementation and its performance analysis are presented. The targeted FPGA is Altera CycloneIV. The obtained design results in superior performance, greater data sample frequency and less logic occupation.
Estimated spectrum adaptive postfilter and the iterative prepost filtering algirighms
Linares, Irving (Inventor)
2004-01-01
The invention presents The Estimated Spectrum Adaptive Postfilter (ESAP) and the Iterative Prepost Filter (IPF) algorithms. These algorithms model a number of image-adaptive post-filtering and pre-post filtering methods. They are designed to minimize Discrete Cosine Transform (DCT) blocking distortion caused when images are highly compressed with the Joint Photographic Expert Group (JPEG) standard. The ESAP and the IPF techniques of the present invention minimize the mean square error (MSE) to improve the objective and subjective quality of low-bit-rate JPEG gray-scale images while simultaneously enhancing perceptual visual quality with respect to baseline JPEG images.
Performance Evaluation of 2D Adaptive Bilateral Filter For Removal of Noise From Robust Images
Sridhar, B.; Dr.K.V.V.S.Reddy
2013-01-01
In this paper, we present the performance analysis of adaptive bilateral filter by pixel to noise ratio and mean square errors. It was evaluate changing the parameters of the adaptive filter half width values and standard deviations. In adaptive bilateral filter, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The variance of range filter can also be adaptive. The filter is applied to improve the sharpens of a gray level and color im...
Adaptive robust Kalman filtering for precise point positioning
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)
Adaptive monogenic filtering and normalization of ESPI fringe patterns
Guerrero, J. A.; Marroquin, J. L.; Rivera, M.; Quiroga, J. A.
2005-11-01
A technique is presented for filtering and normalizing noisy fringe patterns, which may include closed fringes, so that single-frame demodulation schemes may be successfully applied. It is based on the construction of an adaptive filter as a linear combination of the responses of a set of isotropic bandpass filters. The space-varying coefficients are proportional to the envelope of the response of each filter, which in turn is computed by using the corresponding monogenic image [Felsberg and Sommer, IEEE Trans. Signal Process.49, 3136 (2001)]. Some examples of demodulation of real Electronic Speckle Pattern Interferometry (ESPI) images patterns are presented.
Adaptive Filtering for Non-Gaussian Processes
Kidmose, Preben
2000-01-01
A new stochastic gradient robust filtering method, based on a non-linear amplitude transformation, is proposed. The method requires no a priori knowledge of the characteristics of the input signals and it is insensitive to the signals distribution and to the stationarity of the signals. A...... simulation study, applying both synthetic and real-world signals, shows that the proposed method has overall better robustness performance, in terms of modeling error, compared with state-of-the-art robust filtering methods. A remarkable property of the proposed method is that it can handle double-talk in...
Adaptive Filtering for Aeroservoelastic Response Suppression Project
National Aeronautics and Space Administration — CSA Engineering proposes the design of an adaptive aeroelastic mode suppression for advanced fly-by-wire aircraft, which will partition the modal suppression...
An Efficient Adaptive Filtering for CFA Demosaicking
Dev. R. Newlin,; Elwin Chandra Monie
2010-01-01
Most digital still cameras acquire imagery with a color filter array (CFA), sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the highfrequency information of imagery as much as possible, since such information is essential for image visual quality. We discuss in this paper two key observations for preserving high-frequency inf...
Adaptive Control Using Residual Mode Filters Applied to Wind Turbines
Frost, Susan A.; Balas, Mark J.
2011-01-01
Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a model reference direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will augment the adaptive controller using a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. We apply these theoretical results to design an adaptive collective pitch controller for a high-fidelity simulation of a utility-scale, variable-speed wind turbine that has minimum phase zeros.
Junction-type photonic crystal waveguides for notch- and pass-band filtering
Shahid, Naeem
2011-01-01
Evolution of the mode gap and the associated transmission mini stop-band (MSB) as a function of photonic crystal (PhC) waveguide width is theoretically and experimentally investigated. The change of line-defect width is identified to be the most appropriate way since it offers a wide MSB wavelength tuning range. A high transmission narrow-band filter is experimentally demonstrated in a junction-type waveguide composed of two PhC waveguides with slightly different widths. The full width at half maximum is 5.6 nm; the peak transmission is attenuated by only ∼5 dB and is ∼20 dB above the MSBs. Additionally, temperature tuning of the filter were also performed. The results show red-shift of the transmission peak and the MSB edges with a gradient of dλ/dT = 0.1 nm/°C. It is proposed that the transmission MSBs in such junction-type cascaded PhC waveguides can be used to obtain different types of filters. © 2011 Optical Society of America.
Performance Evaluation Of Different Adaptive Filters For ECG Signal Processing
Sachin singh,
2010-08-01
Full Text Available One of the main problem in biomedical data processing like electrocardiography is the separation of the wanted signal from noises caused by power line interference, external electromagnetic fields and random body movements and respiration. Different types of digital filters are used to remove signal components from unwanted frequency ranges. It is difficult to apply filters with fixed coefficients to reduce Biomedical Signal noises, because human behavior is not exact known depending on the time. Adaptive filter technique is required to overcome this problem. In this paper two types of adaptive filters are considered to reduce the ECG signal noises like PLI and Base Line Interference. Results of simulations inMATLAB are presented.
Adaptive Gaussian Mixture Filter Based on Statistical Linearization
Huber, Marco F.
2012-01-01
Gaussian mixtures are a common density representation in nonlinear, non-Gaussian Bayesian state estimation. Selecting an appropriate number of Gaussian components, however, is difficult as one has to trade of computational complexity against estimation accuracy. In this paper, an adaptive Gaussian mixture filter based on statistical linearization is proposed. Depending on the nonlinearity of the considered estimation problem, this filter dynamically increases the number of components via spli...
Multimodal Medical Image Fusion by Adaptive Manifold Filter
Peng Geng; Shuaiqi Liu; Shanna Zhuang
2015-01-01
Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally,...
A nonlinear neural fir filter with an adaptive activation function
Lee Su Goh
2003-01-01
Full Text Available An adaptive amplitude normalized nonlinear gradient descent (AANNGD algorithm for the class of nonlinear finite impulse response (FIR adaptive filters (dynamical perception is introduced. This is achieved by making the amplitude of the nonlinear activation function gradient adaptive. The proposed learning algorithm is suitable for processing of nonlinear and nonstationary signals with a large dynamical range, and removes the unwanted effect of saturation nonlinearities. For rigor, sensitivity analysis is performed and the improved performance of the AANNGD algorithm over the standard LMS, NGD, NNGD, the fully adaptive NNGD (FANNGD and the sign algorithm is verified by simulations on nonlinear and nonstationary inputs with large dynamics.
Artifact removal from EEG signals using adaptive filters in cascade
Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records
Improvement of the Simplified Fast Transversal Filter Type Algorithm for Adaptive Filtering
Madjid Arezki
2009-01-01
Full Text Available Problem statement: In this study, we proposed a new algorithm M-SMFTF for adaptive filtering with fast convergence and low complexity. Approach: It was the result of a simplified FTF type algorithm, where the adaptation gain was obtained only from the forward prediction variables and using a new recursive method to compute the likelihood variable. Results: The computational complexity was reduced from 7L-6L, where L is the finite impulse response filter length. Furthermore, this computational complexity can be significantly reduced to (2L+4P when used with a reduced P-size forward predictor. Conclusion: This algorithm presented a certain interest, for the adaptation of very long filters, like those used in the problems of echo acoustic cancellation, due to its reduced complexity, its numerical stability and its convergence in the presence of the speech signal.
Design and Efficient Implementation of Oversampled GDFT Filter Banks for Subband Adaptive Filtering
Weiss, S; Harteneck, M; Stewart, R W
1998-01-01
This paper introduces a polyphase implementation and design of an oversampled K-channel generalized DFT (GDFT) filter bank, which can be employed for subband adaptive filtering, and therefore is required to have a low aliasing level in the subband signals. A polyphase structure is derived which can be factorized into a real valued polyphase network and a GDFT modulation. For the latter, an FFT realization may be used, yielding a highly efficient polyphase implementation for arbitrary integer ...
Adaptive Compensation of Reactive Power With Shunt Active Power Filters
Blaabjerg, Frede; Asiminoaei, Lucian; Hansen, Steffan;
2008-01-01
This paper describes an adaptive method for compensating the reactive power with an active power filter (APF), which is initially rated for mitigation of only the harmonic currents given by a nonlinear industrial load. It is proven that, if the harmonic currents do not load the APF at the rated...
An adaptive Kalman filter for speckle reductions in ultrasound images
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
BPSK Receiver Based on Recursive Adaptive Filter with Remodulation
N. Milosevic
2011-12-01
Full Text Available This paper proposes a new binary phase shift keying (BPSK signal receiver intended for reception under conditions of significant carrier frequency offsets. The recursive adaptive filter with least mean squares (LMS adaptation is used. The proposed receiver has a constant, defining the balance between the recursive and the nonrecursive part of the filter, whose proper choice allows a simple construction of the receiver. The correct choice of this parameter could result in unitary length of the filter. The proposed receiver has performance very close to the performance of the BPSK receiver with perfect frequency synchronization, in a wide range of frequency offsets (plus/minus quarter of the signal bandwidth. The results obtained by the software simulation are confirmed by the experimental results measured on the receiver realized with the universal software radio peripheral (USRP, with the baseband signal processing at personal computer (PC.
Adaptive training of feedforward neural networks by Kalman filtering
Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.)
Bridging the ensemble Kalman filter and particle filters: the adaptive Gaussian mixture filter
Stordal, Andreas Størksen; Karlsen, Hans A.; Nævdal, Geir; Skaug, Hans J.; Vallès, Brice
2010-01-01
The nonlinear filtering problem occurs in many scientific areas. Sequential Monte Carlo solutions with the correct asymptotic behavior such as particle filters exist, but they are computationally too expensive when working with high-dimensional systems. The ensemble Kalman filter (EnKF) is a more robust method that has shown promising results with a small sample size, but the samples are not guaranteed to come from the true posterior distribution. By approximating the model error with a Gauss...
This paper describes ORNL's development of an environment for the simulation of robotic manipulators. Simulation includes the modeling of kinematics, dynamics, sensors, actuators, control systems, operators, and environments. Models will be used for manipulator design, proposal evaluation, control system design and analysis, graphical preview of proposed motions, safety system development, and training. Of particular interest is the development of models for robotic manipulators having at least one flexible link. As a first application, models have been developed for the Pacific Northwest Laboratory's Flexible Beam Test Bed (PNL FBTB), which is a 1-Degree-of-Freedom, flexible arm with a hydraulic base actuator. ORNL transferred control algorithms developed for the PNL FBTB to controlling IGRIP models. A robust notch filter is running in IGRIP controlling a full dynamics model of the PNL test bed. Model results provide a reasonable match to the experimental results (quantitative results are being determined) and can run on ORNL's Onyx machine in approximately realtime. The flexible beam is modeled as six rigid sections with torsional springs between each segment. The spring constants were adjusted to match the physical response of the flexible beam model to the experimental results. The controller is able to improve performance on the model similar to the improvement seen on the experimental system. Some differences are apparent, most notably because the IGRIP model presently uses a different trajectory planner than the one used by ORNL on the PNL test bed. In the future, the trajectory planner will be modified so that the experiments and models are the same. The successful completion of this work provides the ability to link C code with IGRIP, thus allowing controllers to be developed, tested, and tuned in simulation and then ported directly to hardware systems using the C language
Paulchamy Balaiah
2012-01-01
Full Text Available Problem statement: This study presents an effective method for removing mixed artifacts (EOG-Electro-ocular gram, ECG-Electrocardiogram, EMG-Electromyogram from the EEG-Electroencephalogram records. The noise sources increases the difficulty in analyzing the EEG and obtaining clinical information. EEG signals are multidimensional, non-stationary (i.e., statistical properties are not invariant in time, time domain biological signals, which are not reproducible. It is supposed to contain information about what is going on in the ensemble of excitatory pyramidal neuron level, at millisecond temporal resolution scale. Since scalp EEG contains considerable amount of noise and artifacts and exactly where it is coming from is poorly determined, extracting information from it is extremely challenging. For this reason it is necessary to design specific filters to decrease such artifacts in EEG records. Approach: Some of the other methods that are really appealing are artifact removal through Independent Component Analysis (ICA, Wavelet Transforms, Linear filtering and Artificial Neural Networks. ICA method could be used in situations, where large numbers of noises need to be distinguished, but it is not suitable for on-line real time application like Brain Computer Interface (BCI. Wavelet transforms are suitable for real-time application, but there all success lies in the selection of the threshold function. Linear filtering is best when; the frequency of noises does not interfere or overlap with each other. In this study we proposed adaptive filtering and neuro-fuzzy filtering method to remove artifacts from EEG. Adaptive filter performs linear filtering. Neuro-fuzzy approaches are very promising for non-linear filtering of noisy image. The multiple-output structure is based on recursive processing. It is able to adapt the filtering action to different kinds of corrupting noise. Fuzzy reasoning embedded into the network structure aims at reducing errors
Wagner D.
2015-01-01
Full Text Available Sensitive millimeter wave diagnostics need often to be protected against unwanted radiation like, for example, stray radiation from high power Electron Cyclotron Heating applied in nuclear fusion plasmas. A notch filter based on a waveguide Bragg reflector (photonic band-gap may provide several stop bands of defined width within up to two standard waveguide frequency bands. A Bragg reflector that reflects an incident fundamental TE11 into a TM1n mode close to cutoff is combined with two waveguide tapers to fundamental waveguide diameter. Here the fundamental TE11 mode is the only propagating mode at both ends of the reflector. The incident TE11 mode couples through the taper and is converted to the high order TM1n mode by the Bragg structure at the specific Bragg resonances. The TM1n mode is trapped in the oversized waveguide section by the tapers. Once reflected at the input taper it will be converted back into the TE11 mode which then can pass through the taper. Therefore at higher order Bragg resonances, the filter acts as a reflector for the incoming TE11 mode. Outside of the Bragg resonances the TE11 mode can propagate through the oversized waveguide structure with only very small Ohmic attenuation compared to propagating in a fundamental waveguide. Coupling to other modes is negligible in the non-resonant case due to the small corrugation amplitude (typically 0.05·λ0, where λ0 is the free space wavelength. A Bragg reflector for 105 and 140 GHz was optimized by mode matching (scattering matrix simulations and manufactured by SWISSto12 SA, where the required mechanical accuracy of ± 5 μm could be achieved by stacking stainless steel rings, manufactured by micro-machining, in a high precision guiding pipe. The two smooth-wall tapers were fabricated by electroforming. Several measurements were performed using vector network analyzers from Agilent (E8362B, ABmm (MVNA 8-350 and Rohde&Schwarz (ZVA24 together with frequency multipliers. The
Demystifying the Digital Adaptive Filters Conducts in Acoustic Echo Cancellation
Mohammad Anamul Haque
2010-12-01
Full Text Available A digital sound system resembles the wireless network link transmission system. A wireless network link is affected by several disturbing factors: fading, attenuation, non-linear distortion and noise. These factors are also unavoidable in acoustic echo cancellation. A few number of digital adaptive filter algorithms are approached to detect and cancel the noise in a system. Among these algorithms Least Mean Square, Block Frequency Domain Adaptive Filter and Kalman Filter are widely used to predict and remove the noise or unwanted signals. It is found that performances of these filters varied and depends upon the system of application. Therefore, a detailed performance analysis is exigent. The goal of this paper is to use an approach to analyze and figure out the best performed filter by performance evaluation in the context of acoustic echo cancellation.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems
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.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-01-01
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. PMID:27472336
Chen, Yangkang
2016-04-01
The seislet transform has been demonstrated to have a better compression performance for seismic data compared with other well-known sparsity promoting transforms, thus it can be used to remove random noise by simply applying a thresholding operator in the seislet domain. Since the seislet transform compresses the seismic data along the local structures, the seislet thresholding can be viewed as a simple structural filtering approach. Because of the dependence on a precise local slope estimation, the seislet transform usually suffers from low compression ratio and high reconstruction error for seismic profiles that have dip conflicts. In order to remove the limitation of seislet thresholding in dealing with conflicting-dip data, I propose a dip-separated filtering strategy. In this method, I first use an adaptive empirical mode decomposition based dip filter to separate the seismic data into several dip bands (5 or 6). Next, I apply seislet thresholding to each separated dip component to remove random noise. Then I combine all the denoised components to form the final denoised data. Compared with other dip filters, the empirical mode decomposition based dip filter is data-adaptive. One only need to specify the number of dip components to be separated. Both complicated synthetic and field data examples show superior performance of my proposed approach than the traditional alternatives. The dip-separated structural filtering is not limited to seislet thresholding, and can also be extended to all those methods that require slope information.
Chen, Yangkang
2016-07-01
The seislet transform has been demonstrated to have a better compression performance for seismic data compared with other well-known sparsity promoting transforms, thus it can be used to remove random noise by simply applying a thresholding operator in the seislet domain. Since the seislet transform compresses the seismic data along the local structures, the seislet thresholding can be viewed as a simple structural filtering approach. Because of the dependence on a precise local slope estimation, the seislet transform usually suffers from low compression ratio and high reconstruction error for seismic profiles that have dip conflicts. In order to remove the limitation of seislet thresholding in dealing with conflicting-dip data, I propose a dip-separated filtering strategy. In this method, I first use an adaptive empirical mode decomposition based dip filter to separate the seismic data into several dip bands (5 or 6). Next, I apply seislet thresholding to each separated dip component to remove random noise. Then I combine all the denoised components to form the final denoised data. Compared with other dip filters, the empirical mode decomposition based dip filter is data-adaptive. One only needs to specify the number of dip components to be separated. Both complicated synthetic and field data examples show superior performance of my proposed approach than the traditional alternatives. The dip-separated structural filtering is not limited to seislet thresholding, and can also be extended to all those methods that require slope information.
Cahit Tağı ÇELİK
2004-01-01
Full Text Available Monitoring the Crustal Movement in Geodesy is performed by the deformation survey and analysis. If monitoring the crustal movements involves more than two epochs of survey campaign then from the plate tectonic theory, stations do not move randomly from one epoch to the other, therefore Kalman Filter may be suitable to use. However, if sudden movements happened in the crust in particular earthquake happened, the crust moves very fast in a very short period of time. When Kalman Filter used for monitoring these movements, from associated epoch, for a number of epochs the results may be biased. In the paper, comparison of two methods for elimination of the above mentioned biases have been performed. These methods are Fading Memory Filter and Adaptive Kalman Filter for an unknown bias.
Market Risk Beta Estimation using Adaptive Kalman Filter
Atanu Das,
2010-06-01
Full Text Available Market risk of an asset or portfolio is recognized through beta in Capital Asset Pricing Model (CAPM. Traditional estimation techniques emerge poor results when beta in CAPM assumed to be dynamic and follows auto regressive model. Kalman Filter (KF can optimally estimate dynamic beta where measurement noise covariance and state noise covariance are assumed to be known in a state-space framework. This paper applied Adaptive Kalman Filter (AKF for beta estimation when the above covariances are not known and estimated dynamically. The technique is first characterized through simulation study and then applied to empirical data from Indian security market. A odification of the used AKF is also proposed to take care of the problems of AKF implementation onbeta estimation and simulations show that modified method improves the performance of the filter measured by RMSE.
Kalman filtering to suppress spurious signals in Adaptive Optics control
Poyneer, L; Veran, J P
2010-03-29
In many scenarios, an Adaptive Optics (AO) control system operates in the presence of temporally non-white noise. We use a Kalman filter with a state space formulation that allows suppression of this colored noise, hence improving residual error over the case where the noise is assumed to be white. We demonstrate the effectiveness of this new filter in the case of the estimated Gemini Planet Imager tip-tilt environment, where there are both common-path and non-common path vibrations. We discuss how this same framework can also be used to suppress spatial aliasing during predictive wavefront control assuming frozen flow in a low-order AO system without a spatially filtered wavefront sensor, and present experimental measurements from Altair that clearly reveal these aliased components.
Infinite impulse response modal filtering in visible adaptive optics
Agapito, G; Quirós-Pacheco, F; Puglisi, A; Esposito, S
2012-01-01
Diffraction limited resolution adaptive optics (AO) correction in visible wavelengths requires a high performance control. In this paper we investigate infinite impulse response filters that optimize the wavefront correction: we tested these algorithms through full numerical simulations of a single-conjugate AO system comprising an adaptive secondary mirror with 1127 actuators and a pyramid wavefront sensor (WFS). The actual practicability of the algorithms depends on both robustness and knowledge of the real system: errors in the system model may even worsen the performance. In particular we checked the robustness of the algorithms in different conditions, proving that the proposed method can reject both disturbance and calibration errors.
Infinite impulse response modal filtering in visible adaptive optics
Agapito, G.; Arcidiacono, C.; Quirós-Pacheco, F.; Puglisi, A.; Esposito, S.
2012-07-01
Diffraction limited resolution adaptive optics (AO) correction in visible wavelengths requires a high performance control. In this paper we investigate infinite impulse response filters that optimize the wavefront correction: we tested these algorithms through full numerical simulations of a single-conjugate AO system comprising an adaptive secondary mirror with 1127 actuators and a pyramid wavefront sensor (WFS). The actual practicability of the algorithms depends on both robustness and knowledge of the real system: errors in the system model may even worsen the performance. In particular we checked the robustness of the algorithms in different conditions, proving that the proposed method can reject both disturbance and calibration errors.
Adaptive control of large space structures using recursive lattice filters
Sundararajan, N.; Goglia, G. L.
1985-01-01
The use of recursive lattice filters for identification and adaptive control of large space structures is studied. Lattice filters were used to identify the structural dynamics model of the flexible structures. This identification model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures is control engaged. This type of validation scheme prevents instability when the overall loop is closed. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods. The method uses the Linear Quadratic Guassian/Loop Transfer Recovery (LQG/LTR) approach to ensure stability against unmodeled higher frequency modes and achieves the desired performance.
Adaptive Neuro-Fuzzy Extended Kalman Filtering for Robot Localization
Havangi, Ramazan; Teshnehlab, Mohammad
2010-01-01
Extended Kalman Filter (EKF) has been a popular approach to localization a mobile robot. However, the performance of the EKF and the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices (Qk and Rk, respectively). Imprecise knowledge of these statistics can cause significant degradation in performance. This paper proposed the development of an Adaptive Neuro- Fuzzy Extended Kalman Filtering (ANFEKF) for localization of robot. The Adaptive Neuro-Fuzzy attempts to estimate the elements of Qk and Rk matrices of the EKF algorithm, at each sampling instant when measurement update step is carried out. The ANFIS supervises the performance of the EKF with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (SD) to minimize the differences of the actual value of the covariance of the residual with its theoretical value as...
Reduced-Rank Adaptive Filtering Using Krylov Subspace
Sergueï Burykh; Karim Abed-Meraim
2003-01-01
A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practical...
Reduced-Rank Adaptive Filtering Using Krylov Subspace
Burykh Sergueï; Abed-Meraim Karim
2002-01-01
A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practica...
Adaptive Kalman filtering for anomaly detection in software appliances
Knorn, Florian; Leith, Douglas J.
2008-01-01
Availability and reliability are often important features of key software appliances such as firewalls, web servers, etc. In this paper we seek to go beyond the simple heartbeat monitoring that is widely used for failover control. We do this by integrating more fine grained measurements that are readily available on most platforms to detect possible faults or the onset of failures. In particular, we evaluate the use of adaptive Kalman Filtering for automated CPU usage prediction that...
Model Adaptation for Prognostics in a Particle Filtering Framework
Bhaskar Saha
2011-01-01
Full Text Available One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion and Li-Polymer batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.
Yingsong Li
2013-01-01
Full Text Available A printed reconfigurable ultra-wideband (UWB monopole antenna with triple narrow band-notched characteristics is proposed for cognitive radio applications in this paper. The triple narrow band-notched frequencies are obtained using a defected microstrip structure (DMS band stop filter (BSF embedded in the microstrip feed line and an inverted π-shaped slot etched in the rectangular radiation patch, respectively. Reconfigurable characteristics of the proposed cognitive radio antenna (CRA are achieved by means of four ideal switches integrated on the DMS-BSF and the inverted π-shaped slot. The proposed UWB CRA can work at eight modes by controlling switches ON and OFF. Moreover, impedance bandwidth, design procedures, and radiation patterns are presented for analysis and explanation of this antenna. The designed antenna operates over the frequency band between 3.1 GHz and 14 GHz (bandwidth of 127.5%, with three notched bands from 4.2 GHz to 6.2 GHz (38.5%, 6.6 GHz to 7.0 GHz (6%, and 12.2 GHz to 14 GHz (13.7%. The antenna is successfully simulated, fabricated, and measured. The results show that it has wide impedance bandwidth, multimodes characteristics, stable gain, and omnidirectional radiation patterns.
Adaptive distributed Kalman filtering with wind estimation for astronomical adaptive optics.
Massioni, Paolo; Gilles, Luc; Ellerbroek, Brent
2015-12-01
In the framework of adaptive optics (AO) for astronomy, it is a common assumption to consider the atmospheric turbulent layers as "frozen flows" sliding according to the wind velocity profile. For this reason, having knowledge of such a velocity profile is beneficial in terms of AO control system performance. In this paper we show that it is possible to exploit the phase estimate from a Kalman filter running on an AO system in order to estimate wind velocity. This allows the update of the Kalman filter itself with such knowledge, making it adaptive. We have implemented such an adaptive controller based on the distributed version of the Kalman filter, for a realistic simulation of a multi-conjugate AO system with laser guide stars on a 30 m telescope. Simulation results show that this approach is effective and promising and the additional computational cost with respect to the distributed filter is negligible. Comparisons with a previously published slope detection and ranging wind profiler are made and the impact of turbulence profile quantization is assessed. One of the main findings of the paper is that all flavors of the adaptive distributed Kalman filter are impacted more significantly by turbulence profile quantization than the static minimum mean square estimator which does not incorporate wind profile information. PMID:26831389
Switched Band-Pass Filters for Adaptive Transceivers
Wang, Ray
2007-01-01
Switched band-pass filters are key components of proposed adaptive, software- defined radio transceivers that would be parts of envisioned digital-data-communication networks that would enable real-time acquisition and monitoring of data from geographically distributed sensors. Examples of sensors to be connected to such networks include security cameras, radio-frequency identification units, and geolocation units based on the Global Positioning System. Through suitable software configuration and without changing hardware, these transceivers could be made to operate according to any of a number of complex wireless-communication standards that could be characterized by diverse modulation schemes, bandwidths, and data-handling protocols. The adaptive transceivers would include field-programmable gate arrays (FPGAs) and digital signal-processing hardware. In the receiving path of a transceiver, the incoming signal would be amplified by a low-noise amplifier (LNA). The output spectrum of the LNA would be processed by a band-pass filter operating in the frequency range between 900 MHz and 2.4 GHz. Then a down-converter would translate the signal to a lower frequency range to facilitate analog-to-digital conversion, which would be followed by baseband processing by one or more FPGAs. In the transmitting path, a digital stream would first be converted to an analog signal, which would then be up-converted to a selected frequency band before being applied to a transmitting power amplifier. The aforementioned band-pass filter in the receiving path would be a combination of resonant inductor-and-capacitor filters and switched band-pass filters. The overall combination would implement a switch function designed mathematically to exhibit desired frequency responses and to switch the signal in each frequency band to an analog-to-digital converter appropriate for that band to produce a digital intermediate-frequency signal for digital signal processing.
Coevolution-Based Adaptive Particle Filters for Global Localization
LUORonghua; HONGBingrong; PIAOSonghao; DAIHuming
2005-01-01
A coevolution mechanism derived from competition relationships between ecological species is merged into Particle filters (PF). The new version of particle filters is termed Coevolutionbased adaptive particle filters (CEAPF). In CEAPF, samples are clustered into species, each of which represents a hypothesis of state of the system in a higher level than a single sample. Since the coevolution between the species ensures that the multiple distinct hypotheses can be tracked stably, the problem of premature convergence of PF can be solved. And the number of samples can be adjusted adaptively over time according to the uncertainty of the state of the system by using the population growth model. In addition, by using the crossover and mutation operators in evolutionary computation, intra-species evolution can drive the samples move towards the regions where the desired posterior density is large. So a small number of samples can represent the desired density well enough. And CEAPF is applied to robot localization in highly symmetric environments. Experiments prove that CEAPF can considerably improve the success rate and precision of localization.
Meng, Yang; Gao, Shesheng; Zhong, Yongmin; Hu, Gaoge; Subic, Aleksandar
2016-03-01
The use of the direct filtering approach for INS/GNSS integrated navigation introduces nonlinearity into the system state equation. As the unscented Kalman filter (UKF) is a promising method for nonlinear problems, an obvious solution is to incorporate the UKF concept in the direct filtering approach to address the nonlinearity involved in INS/GNSS integrated navigation. However, the performance of the standard UKF is dependent on the accurate statistical characterizations of system noise. If the noise distributions of inertial instruments and GNSS receivers are not appropriately described, the standard UKF will produce deteriorated or even divergent navigation solutions. This paper presents an adaptive UKF with noise statistic estimator to overcome the limitation of the standard UKF. According to the covariance matching technique, the innovation and residual sequences are used to determine the covariance matrices of the process and measurement noises. The proposed algorithm can estimate and adjust the system noise statistics online, and thus enhance the adaptive capability of the standard UKF. Simulation and experimental results demonstrate that the performance of the proposed algorithm is significantly superior to that of the standard UKF and adaptive-robust UKF under the condition without accurate knowledge on system noise, leading to improved navigation precision.
Samuel Boudet
2014-01-01
Full Text Available Muscle artifacts constitute one of the major problems in electroencephalogram (EEG examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings.
Statistical-uncertainty-based adaptive filtering of lidar signals
An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with in situ instruments located 320 m from the lidar. The micrometerological humidity data were used to calibrate the ratio of the lidar gains of the H2O and the N2 photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem. (c) 2000 Optical Society of America
Reduced rank adaptive filtering in impulsive noise environments
Soury, Hamza
2014-11-01
An impulsive noise environment is considered in this paper. A new aspect of signal truncation is deployed to reduce the harmful effect of the impulsive noise to the signal. A full rank direct solution is derived followed by an iterative solution. The reduced rank adaptive filter is presented in this environment by using two methods for rank reduction, while the minimized objective function is defined using the L
Attitude determination using an adaptive multiple model filtering Scheme
Lam, Quang; Ray, Surendra N.
1995-05-01
Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown
SIMULATION AND PERFORMANCE ANALYASIS OF ADAPTIVE FILTER IN NOISE CANCELLATION
RAJ KUMAR THENUA,
2010-09-01
Full Text Available Noise problems in the environment have gained attention due to the tremendous growth of technology that has led to noisy engines, heavy machinery, high speed wind buffeting and other noise sources. The problem of controlling the noise level has become the focus of a tremendous amount of research over the years. In last few years various adaptive algorithms are developed for noise cancellation. In this paper we present an implementation of LMS (Least Mean Square, NLMS (Normalized Least Mean Square and RLS (Recursive Least Square algorithms on MATLAB platform with the intention to compare their performance in noise cancellation. We simulate the adaptive filter in MATLAB with a noisy tone signal and white noise signal and analyze the performance of algorithms in terms of MSE (Mean Squared Error, percentage noise removal, computational complexity and stability. The obtained results shows that RLS has the best performance but at thecost of large computational complexity and memory requirement.
Jinsoo Jeong
2011-06-01
Full Text Available This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end reference signal will then be a whitened sequence to a cascaded adaptive FIR filter in the beamforming structure. By using an inverse kepstrum filter as a whitening filter with the use of a delay filter, the cascaded adaptive FIR filter estimates only the numerator of the polynomial part from the ratio of overall combined transfer functions. The test results have shown that the adaptive FIR filter is more effective in beamforming structure than an adaptive noise cancelling (ANC structure in terms of signal distortion in the desired signal and noise reduction in noise with nonminimum phase components. In addition, the inverse kepstrum method shows almost the same convergence level in estimate of noise statistics with the use of a smaller amount of adaptive FIR filter weights than the kepstrum method, hence it could provide better computational simplicity in processing. Furthermore, the rear-end inverse kepstrum method in beamforming structure has shown less signal distortion in the desired signal than the front-end kepstrum method and the front-end inverse kepstrum method in beamforming structure.
Multimodal Medical Image Fusion by Adaptive Manifold Filter.
Geng, Peng; Liu, Shuaiqi; Zhuang, Shanna
2015-01-01
Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images. PMID:26664494
Multimodal Medical Image Fusion by Adaptive Manifold Filter
Peng Geng
2015-01-01
Full Text Available Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images.
Immune adaptive Gaussian mixture par ticle filter for state estimation
Wenlong Huang; Xiaodan Wang; Yi Wang; Guohong Li
2015-01-01
The particle filter (PF) is a flexible and powerful sequen-tial Monte Carlo (SMC) technique capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. However, the generic PF suffers from particle degeneracy and sample im-poverishment, which greatly affects its performance for nonlinear, non-Gaussian tracking problems. To deal with those issues, an improved PF is proposed. The algorithm consists of a PF that uses an immune adaptive Gaussian mixture model (IAGM) based immune algorithm to re-approximate the posterior density. At the same time, three immune antibody operators are embed in the new filter. Instead of using a resample strategy, the newest obser-vation and conditional likelihood are integrated into those immune antibody operators to update the particles, which can further im-prove the diversity of particles, and drive particles toward their close local maximum of the posterior probability. The improved PF algorithm can produce a closed-form expression for the posterior state distribution. Simulation results show the proposed algorithm can maintain the effectiveness and diversity of particles and avoid sample impoverishment, and its performance is superior to several PFs and Kalman filters.
A wavelet packet adaptive filtering algorithm for enhancing manatee vocalizations.
Gur, M Berke; Niezrecki, Christopher
2011-04-01
Approximately a quarter of all West Indian manatee (Trichechus manatus latirostris) mortalities are attributed to collisions with watercraft. A boater warning system based on the passive acoustic detection of manatee vocalizations is one possible solution to reduce manatee-watercraft collisions. The success of such a warning system depends on effective enhancement of the vocalization signals in the presence of high levels of background noise, in particular, noise emitted from watercraft. Recent research has indicated that wavelet domain pre-processing of the noisy vocalizations is capable of significantly improving the detection ranges of passive acoustic vocalization detectors. In this paper, an adaptive denoising procedure, implemented on the wavelet packet transform coefficients obtained from the noisy vocalization signals, is investigated. The proposed denoising algorithm is shown to improve the manatee detection ranges by a factor ranging from two (minimum) to sixteen (maximum) compared to high-pass filtering alone, when evaluated using real manatee vocalization and background noise signals of varying signal-to-noise ratios (SNR). Furthermore, the proposed method is also shown to outperform a previously suggested feedback adaptive line enhancer (FALE) filter on average 3.4 dB in terms of noise suppression and 0.6 dB in terms of waveform preservation. PMID:21476661
Adaptive Neuro-Fuzzy Extended Kalman Filtering for Robot Localization
Ramazan Havangi
2010-03-01
Full Text Available Extended Kalman Filter (EKF has been a popular approach to localization a mobile robot. However, the performance of the EKF and the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices (Qk and Rk , respectively. Imprecise knowledge of these statistics can cause significant degradation in performance. This paper proposed the development of an Adaptive Neuro- Fuzzy Extended Kalman Filtering (ANFEKF for localization of robot. The Adaptive Neuro-Fuzzy attempts to estimate the elements of Qk and Rk matrices of the EKF algorithm, at each sampling instant when measurement update step is carried out. The ANFIS supervises the performance of the EKF with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (SD to minimize the differences of the actual value of the covariance of the residual with its theoretical value as much possible. The simulation results show the effectiveness of the proposed algorithm.
An adaptive filtered back-projection for photoacoustic image reconstruction
Purpose: The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. Methods: A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors’ new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal’s contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. Results: The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing
Rossi, V
2002-01-01
In the framework of the LHC project and the modifications of the SPS as its injector, I present the concept of global digital signal processing applied to a particle accelerator, using Field Programmable Gate Array (FPGA) technology. The approach of global digital synthesis implements in numerical form the architecture of a system, from the start up of a project and the very beginning of the signal flow. It takes into account both the known parameters and the future evolution, whenever possible. Due to the increased performance requirements of today's projects, the CAE design methodology becomes more and more necessary to handle successfully the added complexity and speed of modern electronic circuits. Simulation is performed both for behavioural analysis, to ensure conformity to functional requirements, and for time signal analysis (speed requirements). The digital notch filter with programmable delay for the SPS Transverse Damper is now fully operational with fixed target and LHC-type beams circulating in t...
An Adaptive Unscented Kalman Filtering Algorithm for MEMS/GPS Integrated Navigation Systems
Jianhua Cheng; Daidai Chen; Rene Jr. Landry; Lin Zhao; Dongxue Guan
2014-01-01
MEMS/GPS integrated navigation system has been widely used for land-vehicle navigation. This system exhibits large errors because of its nonlinear model and uncertain noise statistic characteristics. Based on the principles of the adaptive Kalman filtering (AKF) and unscented Kalman filtering (AUKF) algorithms, an adaptive unscented Kalman filtering (AUKF) algorithm is proposed. By using noise statistic estimator, the uncertain noise characteristics could be online estimated to adaptively com...
Reduced-Rank Adaptive Filtering Using Krylov Subspace
Burykh Sergueï
2002-01-01
Full Text Available A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practical issues are discussed and low-complexity versions are also included in our study. It is believed that the insight developed in this paper can be further used to improve existing reduced-rank methods according to known results in the domain of Krylov subspace methods.
Reduced-Rank Adaptive Filtering Using Krylov Subspace
Burykh, Sergueï; Abed-Meraim, Karim
2003-12-01
A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practical issues are discussed and low-complexity versions are also included in our study. It is believed that the insight developed in this paper can be further used to improve existing reduced-rank methods according to known results in the domain of Krylov subspace methods.
Channel Estimation using Adaptive Filtering for LTE-Advanced
Saqib Saleem
2011-05-01
Full Text Available For demand of high data rates, enhanced system capacity and coverage, ITU made proposal for the standardization of next generation wireless communication systems, known as IMT-Advanced. To achieve these targets, a priori knowledge of the channel is required at the transmitter side. In this paper, three adaptive channel estimation techniques: Least Mean Square (LMS, Recursive Least Square (RLS and Kalman-Filtering Based, are compared in terms of performance and complexity. For performance, Mean Square Error (MSE and Symbol Error Rate (SER while for complexity, computational time is measured for variable channel impulse response (CIR lengths and channel taps. MATLAB Monte-Carlo Simulations are used to evaluate these techniques.
Adaptive integrated navigation filtering based on accelerometer calibration
Qifan Zhou
2012-11-01
Full Text Available In this paper, a novel GPS (Global Positioning System and DR (Dead Reckoning system which was based on the accelerometer and gyroscope integrated system was designed and implemented. In this system, the odometer used in traditional DR system was replaced by a MEMS tri-axis accelerometer in order to decrease the cost and the volume of the system. The system was integrated by the Kalman filter and a new mathematical model was introduced. In order to reasonably use the GPS information, an adaptive algorithm based on single measurement system which could estimate the measurement noise covariance was obtained. On the purpose of reducing the effect of the accumulated error caused by drift and bias of accelerometer, the accelerometer was calibrated online when GPS performed well. In this way, the integrated system could not only obtain the high-precision positioning in real time, but also perform stably in practice.
Adaptive Data Filtering of Inertial Sensors with Variable Bandwidth
Mushfiqul Alam
2015-02-01
Full Text Available MEMS (micro-electro-mechanical system-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU, which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor’s behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer’s data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing.
Adaptive data filtering of inertial sensors with variable bandwidth.
Alam, Mushfiqul; Rohac, Jan
2015-01-01
MEMS (micro-electro-mechanical system)-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU), which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor's behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer's data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing. PMID:25648711
G Huang; Q. Zhang;
2012-01-01
In order to estimate the satellite clock offset in a real-time mode, a new algorithm of adaptively robust Kalman filter with classified adaptive factors for clock offset estimation is proposed. Compared with standard Kalman filter clock offset model, the new method can detect and control outliers and clock jumps automatically in real-time. Moreover, the clock model parameters, which contain the clock offset, clock speed and clock shift, are classified to decide the adaptive factors in the new...
Theory of affine projection algorithms for adaptive filtering
Ozeki, Kazuhiko
2016-01-01
This book focuses on theoretical aspects of the affine projection algorithm (APA) for adaptive filtering. The APA is a natural generalization of the classical, normalized least-mean-squares (NLMS) algorithm. The book first explains how the APA evolved from the NLMS algorithm, where an affine projection view is emphasized. By looking at those adaptation algorithms from such a geometrical point of view, we can find many of the important properties of the APA, e.g., the improvement of the convergence rate over the NLMS algorithm especially for correlated input signals. After the birth of the APA in the mid-1980s, similar algorithms were put forward by other researchers independently from different perspectives. This book shows that they are variants of the APA, forming a family of APAs. Then it surveys research on the convergence behavior of the APA, where statistical analyses play important roles. It also reviews developments of techniques to reduce the computational complexity of the APA, which are important f...
Ida Bagus Ketut Surya Arnawa
2015-08-01
Full Text Available Balinese papyrus (Lontar is one of the most popular media to write for more than a hundred years in Indonesia. Balinese papyrus are used to document things that are considered important in the past. Most of the balinese papyrus suffered damage caused by weathering, edible fungus and insects making it is difficult to read. One of the efforts made to preserve the existence of balinese papyrus is to perform digitization of it. The problems most often encountered in the process of digitizing the image of the balinese papyrus is less good results as there is noise caused by its conditions that have been damaged and the uneven distribution illumination in this part of the image. In this study the authors propose to combine homomorphic filtering with adaptive median filtering to perform image enhancement. Surve results obtained show the percentage of the average respondents stated that the image enhancement results are good is 83.4%, the percentage of the average respondents stated that the image enhancement results are very good is 4% and the percentage of the average respondents stated that the image enhancement results are enough is 12, 6%.
Biparametric Adaptive Filter: detection of compact sources in complex microwave backgrounds
López-Caniego, M.; Vielva, P.
2012-01-01
In this article we consider the detection of compact sources in maps of the Cosmic Microwave Background radiation (CMB) following the philosophy behind the Mexican Hat Wavelet Family (MHWn) of linear filters. We present a new analytical filter, the Biparametric Adaptive Filter (BAF), that is able to adapt itself to the statistical properties of the background as well as to the profile of the compact sources, maximizing the amplification and improving the detection process. We have tested the ...
Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses
Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang
2016-02-01
Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
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. PMID:27438835
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
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. PMID:27438835
Jan eKneissler
2015-04-01
Full Text Available Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF. PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than ten-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.
Mean-square performance of a convex combination of two adaptive filters
Garcia, Jeronimo; Figueiras-Vidal, A.R.; Sayed, A.H.
2006-01-01
Combination approaches provide an interesting way to improve adaptive filter performance. In this paper, we study the mean-square performance of a convex combination of two transversal filters. The individual filters are independently adapted using their own error signals, while the combination is...... adapted by means of a stochastic gradient algorithm in order to minimize the error of the overall structure. General expressions are derived that show that the method is universal with respect to the component filters, i.e., in steady-state, it performs at least as well as the best component filter....... Furthermore, when the correlation between the a priori errors of the components is low enough, their combination is able to outperform both of them. Using energy conservation relations, we specialize the results to a combination of least mean-square filters operating both in stationary and in nonstationary...
Geometric-Algebra LMS Adaptive Filter and Its Application to Rotation Estimation
Lopes, Wilder B.; Al-Nuaimi, Anas; Lopes, Cassio G.
2016-06-01
This paper exploits Geometric (Clifford) Algebra (GA) theory in order to devise and introduce a new adaptive filtering strategy. From a least-squares cost function, the gradient is calculated following results from Geometric Calculus (GC), the extension of GA to handle differential and integral calculus. The novel GA least-mean-squares (GA-LMS) adaptive filter, which inherits properties from standard adaptive filters and from GA, is developed to recursively estimate a rotor (multivector), a hypercomplex quantity able to describe rotations in any dimension. The adaptive filter (AF) performance is assessed via a 3D point-clouds registration problem, which contains a rotation estimation step. Calculating the AF computational complexity suggests that it can contribute to reduce the cost of a full-blown 3D registration algorithm, especially when the number of points to be processed grows. Moreover, the employed GA/GC framework allows for easily applying the resulting filter to estimating rotors in higher dimensions.
Benardini, James N.; Koukol, Robert C.; Schubert, Wayne W.; Morales, Fabian; Klatte, Marlin F.
2012-01-01
A report describes an adaptation of a filter assembly to enable it to be used to filter out microorganisms from a propulsion system. The filter assembly has previously been used for particulates greater than 2 micrometers. Projects that utilize large volumes of nonmetallic materials of planetary protection concern pose a challenge to their bioburden budget, as a conservative specification value of 30 spores per cubic centimeter is typically used. Helium was collected utilizing an adapted filtration approach employing an existing Millipore filter assembly apparatus used by the propulsion team for particulate analysis. The filter holder on the assembly has a 47-mm diameter, and typically a 1.2-5 micrometer pore-size filter is used for particulate analysis making it compatible with commercially available sterilization filters (0.22 micrometers) that are necessary for biological sampling. This adaptation to an existing technology provides a proof-of-concept and a demonstration of successful use in a ground equipment system. This adaptation has demonstrated that the Millipore filter assembly can be utilized to filter out microorganisms from a propulsion system, whereas in previous uses the filter assembly was utilized for particulates greater than 2 micrometers.
两种渐消滤波与自适应抗差滤波的综合比较分析%Comparison of Two Fading Filters and Adaptively Robust Filter
杨元喜; 高为广
2007-01-01
Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the influences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed.
Nababan, Sunfirst Lady Jeanfera
2015-01-01
Basically, every image acquisition can cause to the presence of noise in the resulting image. Uniform Noise, Salt & Pepper Noise, and Speckle Noise are three of many model noises that are present in the image. Digital image that contained noise can cause problems in the form of an image that cannot be interpreted properly by human, however noise can be reduce through image restoration called filtering. Filter method that can be used to reduce the noises are Alpha-Trimmed Mean Filter and Adapt...
Application of Adaptive Divided Difference Filter on GPS/IMU Integrated Navigation System
ZHAO Pei-pei; LI Shi-xin; XIAO Zhi-tao
2009-01-01
The efficient and accurate approximate nonlinear filters have been widely used in the estimation of states and parameters of dynamical systems.In this paper,an adaptive divided difference filter is designed for precise estimation of states and parameters of mieromechanical gyro navigation system.Based on the investigation of nonlinear divided difference filter the adaptive divided difference filter(ADDF)was designed,which takes account of the incorrect time-varying noise statistics of dynamical systems and compensation of the nonlinearity effects neglected by linearization.And its performance is superior to that of DDF and extended Kalman filter (EKF). Simulation results indicate that the advantages of the proposed nonlinear filters make them attractive alternatives to the extended Kalman filter.
An Adjoint-Based Adaptive Ensemble Kalman Filter
Song, Hajoon
2013-10-01
A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters.
Analysis of dynamic deformation processes with adaptive KALMAN-filtering
Eichhorn, Andreas
2007-05-01
In this paper the approach of a full system analysis is shown quantifying a dynamic structural ("white-box"-) model for the calculation of thermal deformations of bar-shaped machine elements. The task was motivated from mechanical engineering searching new methods for the precise prediction and computational compensation of thermal influences in the heating and cooling phases of machine tools (i.e. robot arms, etc.). The quantification of thermal deformations under variable dynamic loads requires the modelling of the non-stationary spatial temperature distribution inside the object. Based upon FOURIERS law of heat flow the high-grade non-linear temperature gradient is represented by a system of partial differential equations within the framework of a dynamic Finite Element topology. It is shown that adaptive KALMAN-filtering is suitable to quantify relevant disturbance influences and to identify thermal parameters (i.e. thermal diffusivity) with a deviation of only 0,2%. As result an identified (and verified) parametric model for the realistic prediction respectively simulation of dynamic temperature processes is presented. Classifying the thermal bend as the main deformation quantity of bar-shaped machine tools, the temperature model is extended to a temperature deformation model. In lab tests thermal load steps are applied to an aluminum column. Independent control measurements show that the identified model can be used to predict the columns bend with a mean deviation (r.m.s.) smaller than 10 mgon. These results show that the deformation model is a precise predictor and suitable for realistic simulations of thermal deformations. Experiments with modified heat sources will be necessary to verify the model in further frequency spectra of dynamic thermal loads.
Farsani, Mahsa Saffari; Sahhaf, Masoud Reza Aghabozorgi; Abootalebi, Vahid
2016-01-01
The aim of this paper is to improve the performance of the conventional Goertzel algorithm in determining the protein coding regions in deoxyribonucleic acid (DNA) sequences. First, the symbolic DNA sequences are converted into numerical signals using electron ion interaction potential method. Then by combining the modified anti-notch filter and linear predictive coding model, we proposed an efficient algorithm to achieve the performance improvement in the Goertzel algorithm for estimating genetic regions. Finally, a thresholding method is applied to precisely identify the exon and intron regions. The proposed algorithm is applied to several genes, including genes available in databases BG570 and HMR195 and the results are compared to other methods based on the nucleotide level evaluation criteria. Results demonstrate that our proposed method reduces the number of incorrect nucleotides which are estimated to be in the noncoding region. In addition, the area under the receiver operating characteristic curve has improved by the factor of 1.35 and 1.12 in HMR195 and BG570 datasets respectively, in comparison with the conventional Goertzel algorithm. PMID:27563569
Development of adaptive IIR filtered-e LMS algorithm for active noise control
SUN Xu; MENG Guang; TENG Pengxiao; CHEN Duanshi
2003-01-01
Compared to finite impulse response (FIR) filters, infinite impulse response (IIR)filters can match the system better with much fewer coefficients, and hence the computationload is saved and the performance improves. Therefore, it is attractive to use IIR filters insteadof FIR filters in active noise control (ANC). However, filtered-U LMS (FULMS) algorithm, theIIR filter-based algorithm commonly used so far cannot ensure global convergence. A new IIRfilter based adaptive algorithm, which can ensure global convergence with computation loadonly slightly increasing, is proposed in this paper. The new algorithm is called as filtered-eLMS algorithm since the error signal of which need to be filtered. Simulation results show thatthe FELMS algorithm presents better performance than the FULMS algorithm.
Slice image pretreatment for cone-beam computed tomography based on adaptive filter
According to the noise properties and the serial slice image characteristics in Cone-Beam Computed Tomography (CBCT) system, a slice image pretreatment for CBCT based on adaptive filter was proposed. The judging criterion for the noise is established firstly. All pixels are classified into two classes: adaptive center weighted modified trimmed mean (ACWMTM) filter is used for the pixels corrupted by Gauss noise and adaptive median (AM) filter is used for the pixels corrupted by impulse noise. In ACWMTM filtering algorithm, the estimated Gauss noise standard deviation in the current slice image with offset window is replaced by the estimated standard deviation in the adjacent slice image to the current with the corresponding window, so the filtering accuracy of the serial images is improved. The pretreatment experiment on CBCT slice images of wax model of hollow turbine blade shows that the method makes a good performance both on eliminating noises and on protecting details. (authors)
Macroblock-based adaptive loop filter for all intra-coding
Jo, Hyun-Ho; Nam, Jung-Hak; Jung, Kwang-Soo; Sim, Dong-Gyu; Choi, Byeong-Doo
2013-03-01
A macroblock-based adaptive loop filter (MBALF) to improve coding gain for all intra-coding was presented. The proposed method adaptively applies the adaptive loop filter for each reconstructed macroblock (MB) in order to reduce the blocking artifact caused by the quantization process. The MBALF can improve coding efficiency of intra-predicted MBs as it is conducted prior to intra-prediction for the subsequent MBs in a slice. We found that the proposed MBALF yields a coding gain of approximately 7.1% compared to the H.264/AVC High profile. In addition, we achieved a coding gain of approximately 8.9%, on average, by combining the conventional in-loop filter, block-based, adaptive filter control.
Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar
2016-01-01
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 Adaptive Unscented Kalman Filtering Algorithm for MEMS/GPS Integrated Navigation Systems
Jianhua Cheng
2014-01-01
Full Text Available MEMS/GPS integrated navigation system has been widely used for land-vehicle navigation. This system exhibits large errors because of its nonlinear model and uncertain noise statistic characteristics. Based on the principles of the adaptive Kalman filtering (AKF and unscented Kalman filtering (AUKF algorithms, an adaptive unscented Kalman filtering (AUKF algorithm is proposed. By using noise statistic estimator, the uncertain noise characteristics could be online estimated to adaptively compensate the time-varying noise characteristics. Employing the adaptive filtering principle into UKF, the nonlinearity of system can be restrained. Simulations are conducted for MEMS/GPS integrated navigation system. The results show that the performance of estimation is improved by the AUKF approach compared with both conventional AKF and UKF.
Microwave Photonic Filters for Interference Cancellation and Adaptive Beamforming
Chang, John
Wireless communication has experienced an explosion of growth, especially in the past half- decade, due to the ubiquity of wireless devices, such as tablets, WiFi-enabled devices, and especially smartphones. Proliferation of smartphones with powerful processors and graphic chips have given an increasing amount of people the ability to access anything from anywhere. Unfortunately, this ease of access has greatly increased mobile wireless bandwidth and have begun to stress carrier networks and spectra. Wireless interference cancellation will play a big role alongside the popularity of wire- less communication. In this thesis, we will investigate optical signal processing methods for wireless interference cancellation methods. Optics provide the perfect backdrop for interference cancellation. Mobile wireless data is already aggregated and transported through fiber backhaul networks in practice. By sandwiching the signal processing stage between the receiver and the fiber backhaul, processing can easily be done locally in one location. Further, optics offers the advantages of being instantaneously broadband and size, weight, and power (SWAP). We are primarily concerned with two methods for interference cancellation, based on microwave photonic filters, in this thesis. The first application is for a co-channel situation, in which a transmitter and receiver are co-located and transmitting at the same frequency. A novel analog optical technique extended for multipath interference cancellation of broadband signals is proposed and experimentally demonstrated in this thesis. The proposed architecture was able to achieve a maximum of 40 dB of cancellation over 200 MHz and 50 dB of cancellation over 10 MHz. The broadband nature of the cancellation, along with its depth, demonstrates both the precision of the optical components and the validity of the architecture. Next, we are interested in a scenario with dynamically changing interference, which requires an adaptive photonic
Baseline adaptive Kalman filter estimation method for nuclear radiation pulse height analysis
To estimate the baseline of sampled pulse signal serial and then complete the analysis of the nuclear radiation pulse height, the present study concerns in baseline adaptive kalman filtering estimation method. The present study concerns in the application effect of using adaptive kalman filtering estimation method to establish the mathematical model and then estimate the baseline. The validity of this method was certified by experiment, this method is an effective way of data pretreatment for the pulse shaping and so on. (authors)
Edited Adaptive Hypermedia: Combining Human and Machine Intelligence to Achieve Filtered Information
Höök, Kristina; Rudström, Åsa; Waern, Annika
1997-01-01
We discuss a novel approach to filtering of hypermedia information based on an information broker and user environment coupled together. The advantage of the proposed approach, edited adaptive hypermedia, is that it combines human expertise with machine intelligence in order to achieve high quality of the filtered information provided to the end users.
A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems with Additive Noise
Yong Zhou
2015-01-01
Full Text Available The Kalman filter (KF, extended KF, and unscented KF all lack a self-adaptive capacity to deal with system noise. This paper describes a new adaptive filtering approach for nonlinear systems with additive noise. Based on the square-root unscented KF (SRUKF, traditional Maybeck’s estimator is modified and extended to nonlinear systems. The square root of the process noise covariance matrix Q or that of the measurement noise covariance matrix R is estimated straightforwardly. Because positive semidefiniteness of Q or R is guaranteed, several shortcomings of traditional Maybeck’s algorithm are overcome. Thus, the stability and accuracy of the filter are greatly improved. In addition, based on three different nonlinear systems, a new adaptive filtering technique is described in detail. Specifically, simulation results are presented, where the new filter was applied to a highly nonlinear model (i.e., the univariate nonstationary growth model (UNGM. The UNGM is compared with the standard SRUKF to demonstrate its superior filtering performance. The adaptive SRUKF (ASRUKF algorithm can complete direct recursion and calculate the square roots of the variance matrixes of the system state and noise, which ensures the symmetry and nonnegative definiteness of the matrixes and greatly improves the accuracy, stability, and self-adaptability of the filter.
Model Adaptation for Prognostics in a Particle Filtering Framework
National Aeronautics and Space Administration — One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated....
Adaptive high temperature superconducting filters for interference rejection
An optically switched high temperature superconducting (HTS) band-reject filter bank is presented. Fast low loss switching of high quality (Q) factor HTS filter elements enables digital selection of arbitrary pass-bands and stop-bands. Patterned pieces of GaAs and silicon are used in the manufacture of the photosensitive switches. Fiber optic cabling is used to transfer the optical energy from an LED to the switch. The fiber optic cable minimizes the thermal loading of the filter package and de-couples the switch's power source from the RF circuit. This paper will discuss the development of a computer-controlled HTS bank of optically switchable, narrow band, high Q bandstop filters which incorporates a cryocooler to maintain the 77 K operating temperature of the HTS microwave circuit
Multi-template Scale-Adaptive Kernelized Correlation Filters
Bibi, Adel Aamer
2015-12-07
This paper identifies the major drawbacks of a very computationally efficient and state-of-the-art-tracker known as the Kernelized Correlation Filter (KCF) tracker. These drawbacks include an assumed fixed scale of the target in every frame, as well as, a heuristic update strategy of the filter taps to incorporate historical tracking information (i.e. simple linear combination of taps from the previous frame). In our approach, we update the scale of the tracker by maximizing over the posterior distribution of a grid of scales. As for the filter update, we prove and show that it is possible to use all previous training examples to update the filter taps very efficiently using fixed-point optimization. We validate the efficacy of our approach on two tracking datasets, VOT2014 and VOT2015.
Performance Evaluation Of Different Adaptive Filters For ECG Signal Processing
Sachin singh,; Dr K. L. Yadav
2010-01-01
One of the main problem in biomedical data processing like electrocardiography is the separation of the wanted signal from noises caused by power line interference, external electromagnetic fields and random body movements and respiration. Different types of digital filters are used to remove signal components from unwanted frequency ranges. It is difficult to apply filters with fixed coefficients to reduce Biomedical Signal noises, because human behavior is not exact known depending on the t...
Multiple Adaptive Fading Schmidt-Kalman Filter for Unknown Bias
Tai-Shan Lou; Zhi-Hua Wang; Meng-Li Xiao; Hui-Min Fu
2014-01-01
Unknown biases in dynamic and measurement models of the dynamic systems can bring greatly negative effects to the state estimates when using a conventional Kalman filter algorithm. Schmidt introduces the “consider” analysis to account for errors in both the dynamic and measurement models due to the unknown biases. Although the Schmidt-Kalman filter “considers” the biases, the uncertain initial values and incorrect covariance matrices of the unknown biases still are not considered. To solve th...
PSO Algorithm based Adaptive Median Filter for Noise Removal in Image Processing Application
Ruby Verma
2016-07-01
Full Text Available A adaptive Switching median filter for salt and pepper noise removal based on genetic algorithm is presented. Proposed filter consist of two stages, a noise detector stage and a noise filtering stage. Particle swarm optimization seems to be effective for single objective problem. Noise Dictation stage works on it. In contrast to the standard median filter, the proposed algorithm generates the noise map of corrupted Image. Noise map gives information about the corrupted and non-corrupted pixels of Image. In filtering, filter calculates the median of uncorrupted neighbouring pixels and replaces the corrupted pixels. Extensive simulations are performed to validate the proposed filter. Simulated results show refinement both in Peak signal to noise ratio (PSNR and Image Quality Index value (IQI. Experimental results shown that proposed method is more effective than existing methods.
IAE-adaptive Kalman filter for INS/GPS integrated navigation system
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.
Adaptive filtering of radar images for autofocus applications
Stiles, J. A.; Frost, V. S.; Gardner, J. S.; Eland, D. R.; Shanmugam, K. S.; Holtzman, J. C.
1981-01-01
Autofocus techniques are being designed at the Jet Propulsion Laboratory to automatically choose the filter parameters (i.e., the focus) for the digital synthetic aperture radar correlator; currently, processing relies upon interaction with a human operator who uses his subjective assessment of the quality of the processed SAR data. Algorithms were devised applying image cross-correlation to aid in the choice of filter parameters, but this method also has its drawbacks in that the cross-correlation result may not be readily interpretable. Enhanced performance of the cross-correlation techniques of JPL was hypothesized given that the images to be cross-correlated were first filtered to improve the signal-to-noise ratio for the pair of scenes. The results of experiments are described and images are shown.
Adaptive Kalman Filter of Transfer Alignment with Un-modeled Wing Flexure of Aircraft
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.
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.
We present design optimization of wavelength filters based on long period waveguide gratings (LPWGs) using the adaptive particle swarm optimization (APSO) technique. We demonstrate optimization of the LPWG parameters for single-band, wide-band and dual-band rejection filters for testing the convergence of APSO algorithms. After convergence tests on the algorithms, the optimization technique has been implemented to design more complicated application specific filters such as erbium doped fiber amplifier (EDFA) amplified spontaneous emission (ASE) flattening, erbium doped waveguide amplifier (EDWA) gain flattening and pre-defined broadband rejection filters. The technique is useful for designing and optimizing the parameters of LPWGs to achieve complicated application specific spectra. (paper)
An Adaptive Kalman Filter Using a Simple Residual Tuning Method
Harman, Richard R.
1999-01-01
One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. A. H. Jazwinski developed a specialized version of this technique for estimation of process noise. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.
In this report we describe the concept of adaptive noise canceling, an alternative method of estimating signals corrupted by additive noise of interference. The method uses 'primary' input containing the corrupted signal and a 'reference' input containing noise correlated in some unknown way with the primary noise, the reference input is adaptively filtered and subtracted from the primary input to obtain the signal estimate. Adaptive filtering before subtraction allows the treatment of inputs that are deterministic or stochastic, stationary or time variable. When the reference input is free of signal and certain other conditions are met then noise in the primary input can be essentially eliminated without signal distortion. It is further shown that the adaptive filter also acts as notch filter. Simulated results illustrate the usefulness of the adaptive noise canceling technique. (author)
Cahit Tağı ÇELİK
2004-01-01
Monitoring the Crustal Movement in Geodesy is performed by the deformation survey and analysis. If monitoring the crustal movements involves more than two epochs of survey campaign then from the plate tectonic theory, stations do not move randomly from one epoch to the other, therefore Kalman Filter may be suitable to use. However, if sudden movements happened in the crust in particular earthquake happened, the crust moves very fast in a very short period of time. When Kalman Filter used for ...
Adaptive RBF Neural Network Control for Three-Phase Active Power Filter
Juntao Fei; Zhe Wang
2013-01-01
An adaptive radial basis function (RBF) neural network control system for three‐phase active power filter (APF) is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non‐linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achie...
Delay Estimator and Improved Proportionate Multi-Delay Adaptive Filtering Algorithm
E. Verteletskaya
2012-04-01
Full Text Available This paper pertains to speech and acoustic signal processing, and particularly to a determination of echo path delay and operation of echo cancellers. To cancel long echoes, the number of weights in a conventional adaptive filter must be large. The length of the adaptive filter will directly affect both the degree of accuracy and the convergence speed of the adaptation process. We present a new adaptive structure which is capable to deal with multiple dispersive echo paths. An adaptive filter according to the present invention includes means for storing an impulse response in a memory, the impulse response being indicative of the characteristics of a transmission line. It also includes a delay estimator for detecting ranges of samples within the impulse response having relatively large distribution of echo energy. These ranges of samples are being indicative of echoes on the transmission line. An adaptive filter has a plurality of weighted taps, each of the weighted taps having an associated tap weight value. A tap allocation/control circuit establishes the tap weight values in response to said detecting means so that only taps within the regions of relatively large distributions of echo energy are turned on. Thus, the convergence speed and the degree of estimation in the adaptation process can be improved.
An Improved Variable Structure Adaptive Filter Design and Analysis for Acoustic Echo Cancellation
A. Kar
2015-04-01
Full Text Available In this research an advance variable structure adaptive Multiple Sub-Filters (MSF based algorithm for single channel Acoustic Echo Cancellation (AEC is proposed and analyzed. This work suggests a new and improved direction to find the optimum tap-length of adaptive filter employed for AEC. The structure adaptation, supported by a tap-length based weight update approach helps the designed echo canceller to maintain a trade-off between the Mean Square Error (MSE and time taken to attain the steady state MSE. The work done in this paper focuses on replacing the fixed length sub-filters in existing MSF based AEC algorithms which brings refinements in terms of convergence, steady state error and tracking over the single long filter, different error and common error algorithms. A dynamic structure selective coefficient update approach to reduce the structural and computational cost of adaptive design is discussed in context with the proposed algorithm. Simulated results reveal a comparative performance analysis over proposed variable structure multiple sub-filters designs and existing fixed tap-length sub-filters based acoustic echo cancellers.
An adaptive filter bank for motor imagery based Brain Computer Interface.
Thomas, Kavitha P; Guan, Cuntai; Tong, Lau Chiew; Prasad, Vinod A
2008-01-01
Brain Computer Interface (BCI) provides an alternative communication and control method for people with severe motor disabilities. Motor imagery patterns are widely used in Electroencephalogram (EEG) based BCIs. These motor imagery activities are associated with variation in alpha and beta band power of EEG signals called Event Related Desynchronization/synchronization (ERD/ERS). The dominant frequency bands are subject-specific and therefore performance of motor imagery based BCIs are sensitive to both temporal filtering and spatial filtering. As the optimum filter is strongly subject-dependent, we propose a method that selects the subject-specific discriminative frequency components using time-frequency plots of Fisher ratio of two-class motor imagery patterns. We also propose a low complexity adaptive Finite Impulse Response (FIR) filter bank system based on coefficient decimation technique which can realize the subject-specific bandpass filters adaptively depending on the information of Fisher ratio map. Features are extracted only from the selected frequency components. The proposed adaptive filter bank based system offers average classification accuracy of about 90%, which is slightly better than the existing fixed filter bank system. PMID:19162856
Secure Tracking in Sensor Networks using Adaptive Extended Kalman Filter
Fard, Ali P
2012-01-01
Location information of sensor nodes has become an essential part of many applications in Wireless Sensor Networks (WSN). The importance of location estimation and object tracking has made them the target of many security attacks. Various methods have tried to provide location information with high accuracy, while lots of them have neglected the fact that WSNs may be deployed in hostile environments. In this paper, we address the problem of securely tracking a Mobile Node (MN) which has been noticed very little previously. A novel secure tracking algorithm is proposed based on Extended Kalman Filter (EKF) that is capable of tracking a Mobile Node (MN) with high resolution in the presence of compromised or colluding malicious beacon nodes. It filters out and identifies the malicious beacon data in the process of tracking. The proposed method considerably outperforms the previously proposed secure algorithms in terms of either detection rate or MSE. The experimental data based on different settings for the netw...
Adaptive filtering enhances information transmission in visual cortex
Sharpee, Tatyana O.; Sugihara, Hiroki; Kurgansky, Andrei V.; Rebrik, Sergei P.; Stryker, Michael P.; Miller, Kenneth D.
2006-01-01
Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain’s coding strategy depends on the stimulus ensemble, we apply a new information-theoretic method that allows unbiased calculation of neural filters (receptive fields) from responses to natural scenes or other complex signals with strong multipoint correlations. In the cat primary visual cortex we comp...
Adaptive Non-Linear Bayesian Filter for ECG Denoising
Mitesh Kumar Sao; Anurag Shrivastava
2014-01-01
The cycles of an electrocardiogram (ECG) signal contain three components P-wave, QRS complex and the T-wave. Noise is present in cardiograph as signals being measured in which biological resources (muscle contraction, base line drift, motion noise) and environmental resources (power line interference, electrode contact noise, instrumentation noise) are normally pollute ECG signal detected at the electrode. Visu-Shrink thresholding and Bayesian thresholding are the two filters based technique ...
Adaptive high-gain extended kalman filter and applications
Boizot, Nicolas Richard
2010-01-01
The work concerns the ``observability problem” --- the reconstruction of a dynamic process's full state from a partially measured state--- for nonlinear dynamic systems. The Extended Kalman Filter (EKF) is a widely-used observer for such nonlinear systems. However it suffers from a lack of theoretical justifications and displays poor performance when the estimated state is far from the real state, e.g. due to large perturbations, a poor initial state estimate, etc… We propose a solution to...
Steady State Analysis of Convex Combination of Affine Projection Adaptive Filters
S. Radhika
2015-05-01
Full Text Available The aim of the study is to propose an adaptive algorithm using convex combinational approach to have both fast convergence and less steady state error simultaneously. For this purpose, we have used two affine projection adaptive filters with complementary nature (both in step size and projection order as the component filters. The first component filter has high projection order and large step size which makes it to have fast convergence at the cost of more steady state error. The second component filter has slow convergence and less steady state error due to the selection of small step size and projection order. Both are combined using convex combiner so as to have best final output with fast convergence and less steady state error. Each of the component filters are updated using their own error signals and stochastic gradient approach is used to update the convex combiner so as to have minimum overall error. By using energy conservation argument, analytical treatment of the combination stage is made in stationary environment. It is found that during initial stage the proposed scheme converges to the fast filter which has good convergence later it converges to either of the two (whichever has less steady state error and towards the end, the final output converges to slow filter which is superior in lesser steady state error. Experimental results proved that the proposed algorithm has adopted the best features of the component filters.
Intra-fraction tumor tracking methods can improve radiation delivery during radiotherapy sessions. Image acquisition for tumor tracking and subsequent adjustment of the treatment beam with gating or beam tracking introduces time latency and necessitates predicting the future position of the tumor. This study evaluates the use of multi-dimensional linear adaptive filters and support vector regression to predict the motion of lung tumors tracked at 30 Hz. We expand on the prior work of other groups who have looked at adaptive filters by using a general framework of a multiple-input single-output (MISO) adaptive system that uses multiple correlated signals to predict the motion of a tumor. We compare the performance of these two novel methods to conventional methods like linear regression and single-input, single-output adaptive filters. At 400 ms latency the average root-mean-square-errors (RMSEs) for the 14 treatment sessions studied using no prediction, linear regression, single-output adaptive filter, MISO and support vector regression are 2.58, 1.60, 1.58, 1.71 and 1.26 mm, respectively. At 1 s, the RMSEs are 4.40, 2.61, 3.34, 2.66 and 1.93 mm, respectively. We find that support vector regression most accurately predicts the future tumor position of the methods studied and can provide a RMSE of less than 2 mm at 1 s latency. Also, a multi-dimensional adaptive filter framework provides improved performance over single-dimension adaptive filters. Work is underway to combine these two frameworks to improve performance.
Gear Fault Signal Detection based on an Adaptive Fractional Fourier Transform Filter
Vibration-based fault diagnosis is widely used for gearbox monitoring. However, it often needs considerable effort to extract effective diagnostic feature signal from noisy vibration signals because of rich signal components contained in a complex gear transmission system. In this paper, an adaptive fractional Fourier transform filter is proposed to suppress noise in gear vibration signals and hence to highlight signal components originated from gear fault dynamic characteristics. The approach relies on the use of adaptive filters in the fractional Fourier transform domain with the optimised fractional transform order and the filter parameters, while the transform orders are selected when the signal have the highest energy gathering and the filter parameters are determined by evolutionary rules. The results from the simulation and experiments have verified the performance of the proposed algorithm in extracting the gear failure signal components from the noisy signals based on a multistage gearbox system.
A proposal for an Adaptive Information Filtering and Control Concept
Maas, H.L.M.M.; Meiler, P.P.
1998-01-01
This paper describes a concept to manage the information exchange between the operators and their consoles (the interface to the computer system) within a Command and Control (C2) centre. Application of his concept will result in a more effective and efficient information exchange, using adaptive in
Adaptive integrated navigation filtering based on accelerometer calibration
Qifan Zhou; Hai Zhang; Yanran Wang
2012-01-01
In this paper, a novel GPS (Global Positioning System) and DR (Dead Reckoning) system which was based on the accelerometer and gyroscope integrated system was designed and implemented. In this system, the odometer used in traditional DR system was replaced by a MEMS tri-axis accelerometer in order to decrease the cost and the volume of the system. The system was integrated by the Kalman filter and a new mathematical model was introduced. In order to reasonably use the GPS information, an adap...
Adaptive filter for a miniature MEMS based attitude and heading reference system
WANG Mei; WANG Yong-quan; ZHANG Yan-hua
2006-01-01
An extended Kalman filter with adaptive gain was used to build a miniature attitude and heading reference system based on a stochastic model. The adaptive filter has six states with a time variable transition matrix. When the system is in the non-acceleration mode, the accelerometer measurements of the gravity and the compass measurements of the heading have observability and yield good estimates of the states. When the system is in the high dynamic mode and the bias has converged to an accurate estimate, the attitude calculation will be maintained for a long interval of time. The adaptive filter tunes its gain automatically based on the system dynamics sensed by the accelerometers to yield optimal performance.
Predicting Hyper-Chaotic Time Series Using Adaptive Higher-Order Nonlinear Filter
Zhang, Jia-Shu; Xiao, Xian-Ci
2001-03-01
A newly proposed method, i.e. the adaptive higher-order nonlinear finite impulse response (HONFIR) filter based on higher-order sparse Volterra series expansions, is introduced to predict hyper-chaotic time series. The effectiveness of using the adaptive HONFIR filter for making one-step and multi-step predictions is tested based on very few data points by computer-generated hyper-chaotic time series including the Mackey-Glass equation and four-dimensional nonlinear dynamical system. A comparison is made with some neural networks for predicting the Mackey-Glass hyper-chaotic time series. Numerical simulation results show that the adaptive HONFIR filter proposed here is a very powerful tool for making prediction of hyper-chaotic time series.