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Kalman filter for parametric fault detection: an internal model principle-based approach

IET CONTROL THEORY AND APPLICATIONS 2012年第5期6卷 715-725页

作者： Doraiswami, R. Cheded, L. Univ New Brunswick Dept Elect & Comp Engn Fredericton NB Canada King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran 31261 Saudi Arabia

The paramount importance of fault detection (FD) in complex engineering systems has undoubtedly been the main driver behind the development of a plethora of techniques in the FD area. In this study, the authors propose an internal model principle-based Kalman filter (IMP-KF) structure for use in the detection of parametric faults. The authors show that the closed-loop structure of the IMP-KF is indeed a necessary and sufficient condition for generating residuals upon which the FD process hinges. They advocate a residual generator structure similar to that used in the standard Kalman filtering (KF), and judiciously exploit the non-robustness to model mismatch of the proposed IMP-KF scheme to detect faults in the presence of noise and disturbances. With no model mismatch, the KF residual's whiteness is exploited to derive a composite hypothesis testing that accounts for a low probability for false alarm and a high probability of correct decision for various reference inputs. The proposed scheme was successfully evaluated on both simulated and physical systems.

关键词： signal processing theory fault diagnosis Other topics in statistics IMP-KF scheme composite hypothesis testing Kalman filters probability parametric fault detection FD area complex engineering system FD process hinges closed-loop structure model mismatching standard Kalman filtering sufficient condition false alarm probability internal model principle-based Kalman filter structure filtering methods in signal processing KF residual whiteness residual generator structure

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Stability of two-dimensional digital filters described by the Fornasini-Marchesini second model with quantisation and overflow

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IET signal processing 2012年第7期6卷 641-647页

作者： Dey, A. Kokil, P. Kar, H. Motilal Nehru Natl Inst Technol Dept Elect & Commun Engn Allahabad 211004 Uttar Pradesh India

This study discusses new criterion for the global asymptotic stability of two-dimensional digital filters described by the Fornasini-Marchesini second local state-space model under various combinations of quantisation... 详细信息

关键词： Fornasini-Marchesini second local state-space model filtering methods in signal processing quantisation nonlinearities two-dimensional digital filters asymptotic stability digital filters overflow nonlinearities signal processing theory global asymptotic stability quantisation (signal) Fornasini-Marchesini second model

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Unsupervised video anomaly detection using feature clustering

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IET signal processing 2012年第5期6卷 521-533页

作者： Li, H. Achim, A. Bull, D. Univ Bristol Dept Elect & Elect Engn Bristol Avon England

This study addresses the problem of automatic anomaly detection for surveillance applications. A general framework for anomalous event detection in uncrowded scenes has been developed which consists of the following key components: (i) an efficient foreground detection model based on a Gaussian mixture model (GMM), which can selectively update pixel information in each image region;(ii) an adaptive foreground object tracker that combines the merits of Kalman, mean-shift and particle filtering;(iii) a feature clustering algorithm, which can automatically choose the optimal number of clusters in the training data for scene pattern modelling;(iv) a statistical scene modeller based on Bayesian theory and GMM, which combines trajectory-based and region-based information for enhanced anomaly detection. The resulting approach achieves fully unsupervised anomaly detection in surveillance video. The experimental results show improved detection performance compared with the state-of-the-art methods.

关键词： anomalous event detection automatic anomaly detection problem efficient foreground detection model Bayesian theory particle filtering unsupervised video anomaly detection belief networks particle filtering (numerical methods) Combinatorial mathematics surveillance video GMM pixel information Kalman filtering filtering methods in signal processing adaptive foreground object tracker Video signal processing Kalman filters feature clustering mean-shift filtering video signal processing statistical scene modeller video surveillance Gaussian mixture model Image recognition general framework uncrowded scenes scene pattern modelling surveillance applications

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Edge detection with multiscale products for SAR image despeckling

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ELECTRONICS LETTERS 2012年第4期48卷 211-U148页

作者： Zhang, W. G. Zhang, Q. Yang, C. S. Changan Univ Coll Geol Engn & Geomat Xian 710054 Peoples R China

A despeckling algorithm of a synthetic aperture radar (SAR) image is proposed, which can effectively preserve edges while cleanly despeckling. The proposed algorithm is robust, simple, and effective, without any a priori information, which employs multiscale products in stationary bandlet transform (SBT) domain to detect edges, and utilises SBT to denoise the edge-removed image. Experimental results show that the visual quality and evaluation indexes of the proposed algorithm outperform that of Lee filtering.

关键词： Lee filtering filtering theory multiscale product visual quality synthetic aperture radar image filtering methods in signal processing SAR image despeckling algorithm speckle radar imaging edge-removed image denoising SBT edge detection image denoising stationary bandlet transform domain Radar equipment, systems and applications synthetic aperture radar Image recognition

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Bayesian track-before-detect algorithm with target amplitude fluctuation based on expectation-maximisation estimation

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IET Radar, Sonar and Navigation 2012年第8期6卷 719-728页

作者： Xia, S.Z. Liu, H.W. National Lab of Radar Signal Processing Xidian University 710071 Shaanxi China

Bayesian track-before-detect is an efficient approach to detect low observable targets. Before implementing Bayesian track-before-detect, one needs to exactly ascertain the target motion model and the measurement model. When the target return amplitude fluctuates, the target return amplitude of the measurement model is not known a priori. In the scenario, standard Bayesian track-before-detect algorithms such as particle filters, which assume perfect knowledge of the model parameters, cannot work well. In this study, the authors propose an expectation-maximisation (EM) algorithm for Bayesian track-before-detect with target amplitude fluctuation, in which the fluctuation models are incorporated into the likelihood function, and the average target return amplitude is estimated by the EM algorithm. The simulation results show that the average target return amplitude can be estimated by the EM algorithm, which is helpful in improving the performance of detection and tracking. Therefore it is feasible to apply the EM algorithm to Bayesian track-before-detect with target amplitude fluctuation. © 2012 The Institution of Engineering and Technology.

关键词： radar signal processing model parameter expectation-maximisation algorithm Bayesian track-before-detect algorithm likelihood function Radar equipment, systems and applications particle filtering (numerical methods) particle filter measurement model target motion model Bayes methods expectation-maximisation estimation filtering methods in signal processing fluctuation model EM algorithm target amplitude fluctuation object detection average target return amplitude observable target detection target tracking Other topics in statistics

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Efficient particle filter algorithm for ultrasonic sensor-based 2D range-only simultaneous localisation and mapping application

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IET WIRELESS SENSOR SYSTEMS 2012年第4期2卷 394-401页

作者： Yang, P. Univ Bedfordshire Fac Creat Arts Technol & Sci Luton Bedfordshire England

Owing to low cost and relatively accurate range measurement, ultrasonic sensors are widely used in various simultaneous localisation and mapping (SLAM) applications. In spite of the abundance of ultrasonic sensor based SLAM applications, a simple and efficient algorithm for an ultrasonic sensor based positioning system with good accuracy and low computational complexity has not yet emerged. The major difficulty is the trade-off between localisation accuracy and computational complexity in most SLAM algorithms, such as extended Kalman filter (EKF) and particle filter. Typically, they improve localisation accuracy by increasing the density of the landmarks, as a result leading to high computational complexity of algorithms and limiting the utilisation of algorithms into online SLAM systems. This study addresses an improved particle filter algorithm to solve ultrasonic sensor based 2D range-only SLAM problem with relatively good accuracy and low computational complexity. This algorithm uses a simple four fixed features based system model to limit the density of the landmarks. A technique called map adjustment is proposed to increase the accuracy and efficiency of the algorithm. Using map adjustment, the proposed particle filter algorithm can improve localisation accuracy and lower computational complexity. The experiment results demonstrate that this algorithm has a better performance than conventional particle filter localisation algorithm.

关键词： nonlinear filters Computational complexity 2D range-only simultaneous localisation and mapping application SLAM applications particle filter algorithm Interpolation and function approximation (numerical analysis) filtering methods in signal processing computational complexity SLAM (robots) Kalman filters extended Kalman filter particle filtering (numerical methods) Robotics signal processing theory range measurement EKF ultrasonic sensor

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Rao-Blackwellised unscented particle filtering for jump Markov non-linear systems: an H_∞ approach

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IET signal processing 2011年第2期5卷 187-193页

作者： Li, W. Jia, Y. Beihang Univ BUAA Res Div 7 Beijing 100191 Peoples R China Beihang Univ BUAA Dept Syst & Control Beijing 100191 Peoples R China Beihang Univ BUAA SMSS Minist Educ Key Lab Math Informat & Behav Semant LMIB Beijing 100191 Peoples R China

In this study, a robust Rao-Blackwellised particle filter (RBPF) is proposed for jump Markov non-linear systems (JMNLSs) with unknown noise statistics. A non-linear filter is presented by applying the unscented transform technique in the H-infinity setting, which is used to update the continuous-state particles in the RBPF framework. Moreover, a way to adaptively adjust the disturbance tolerance level for performance requirement is presented. Simulation results using the proposed approach are also presented.

关键词： noise continuous state particles Optimal control Rao-Blackwellised unscented particle filtering Nonlinear control systems unscented transform technique JMNLS nonlinear control systems H∞ approach nonlinear filter jump Markov nonlinear system RBPF framework particle filtering (numerical methods) filtering methods in signal processing performance requirement signal processing theory H∞ control disturbance tolerance level statistics Markov processes noise statistics Other topics in statistics

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Alignment of non-texture video frames using Kalman filter

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IET COMPUTER VISION 2011年第1期5卷 77-85页

作者： Jianchao, Y. Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Video registration in the presence of several segments of non-texture frames remains a challenging issue, although a wide repertoire of image registration algorithms was developed over the last two decades. In this study, the authors proposed an effective causal scheme of video frames registration, and implemented it by the modified two frames intensity match algorithm. In order to achieve its real-time performance, the authors avoid handling a much more complex appearance model in this causal system by using registration parameters as measurement values, which are estimated directly from two consecutive frames via intensity matching. The simple linear and Gaussian system is thus derived and an efficient Kalman filter algorithm can be utilised. As the Kalman filter naturally incorporated the temporal information contained in a video into the estimation of registration parameters, the algorithm developed in this study is quite robust and has good performance even if the processed video contains several segments of non-texture frames.

关键词： video registration linear systems Kalman filter algorithm Gaussian system image registration linear system Video signal processing filtering methods in signal processing Kalman filters Optical, image and video signal processing nontexture video frames video signal processing intensity match algorithm image registration algorithms

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Parallel-coupled line filter design from an energetic coupling approach

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IET MICROWAVES ANTENNAS & PROPAGATION 2011年第5期5卷 568-575页

作者： Sanchez-Soriano, M. A. Bronchalo, E. Torregrosa-Penalva, G. Miguel Hernandez Univ Signal Theory & Commun Div Elche 03202 Spain

In this study, the design of parallel-coupled line bandpass filters (BPFs) is investigated from an energetic coupling theory approach. The proposed filter design process allows the design of BPFs with improved characteristics. In particular, it is possible to implement filters with reduced occupation area, larger passband bandwidth and improved rejection performance. This is accomplished by controlling the coupling coefficient between resonators by means of the coupling section length. The suggested design technique is validated through the design, fabrication and measurement of several coupled line microstrip BPFs centred at 2.0 GHz.

关键词： rejection performance parallel-coupled line bandpass filter design filtering methods in signal processing energetic coupling theory coupling coefficient microstrip BPF Filters and other networks coupled circuits microstrip filters passband bandwidth Waveguide and microwave transmission line components network synthesis band-pass filters frequency 2.0 GHz

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Non-linear dynamics method for target identification

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IET RADAR SONAR AND NAVIGATION 2011年第7期5卷 741-746页

作者： Carroll, T. L. Rachford, F. J. USN Res Lab Washington DC 20375 USA

One may describe the effect of a radar or sonar target on an incoming signal as a filter that produces a scattered signal. Chaotic signals are very sensitive to the effect of filters, and so a radar or sonar target imposes a unique signature on a scattered chaotic signal. In this study the authors describe a method that uses the concept of phase space dimension to create a reference from a scattered chaotic signal. This reference becomes part of a library, and comparing an unknown scattered signal to this library can reveal which target caused a particular scattered signal. As the authors are not imaging the target, this method can use signal with low-range resolution.

关键词： radar signal processing scattered chaotic signal Sonar and acoustic radar target identification scattered signal filtering methods in signal processing sonar phase space dimension radar nonlinear dynamics method filtering theory chaotic signals sonar target

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