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Continuous-discrete estimation for tracking ballistic missiles in air-surveillance radar

IET RADAR SONAR AND NAVIGATION 2011年第9期5卷 978-986页

作者： Sankowski, M. Przemyslowy Inst Telekomunikacji SA PL-80502 Gdansk Poland

A problem of tracking tactical ballistic missiles (TBMs) in a long-range air-surveillance radar is considered. An effective tracking algorithm is presented, which is based on continuous-time dynamic models of object motions and on a discrete-time model of measurement process. The estimator utilises an innovative combination of kinematic models of TBM movements in radar 'range-azimuth-elevation' coordinates, continuous-discrete extended Kalman filtering, interacting multiple-model state estimation and probabilistic data association. The performance and robustness of the new algorithm was proved by extensively testing using real and simulated radar data. For the purpose of this study its properties are illustrated using simulations.

关键词： extended Kalman filtering continuous-discrete estimation Sensing devices and transducers radar tracking probabilistic data association multiple-model state estimation tactical ballistic missile tracking range-azimuth-elevation coordinates Radar equipment, systems and applications search radar Instrumentation and measurement systems filtering methods in signal processing discrete-time model continuous-time dynamic models measurement systems Kalman filters long-range air-surveillance radar object motions measurement process kinematic models simulated radar data Military radar, detection and tracking systems sensor fusion real radar data

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Recursive architecture using pipeline stall for low complexity MPEG-4 ALS encoder

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ELECTRONICS LETTERS 2011年第9期47卷 567-569页

作者： Kim, D-S Kim, B-S Lee, S-S Choi, B-H KETI Multimedia IP Res Ctr Songnam South Korea

An MPEG-4 audio lossless coding (ALS) encoder consists of a short-term predictor, a joint coder, a long-term predictor, and an entropy coder for audio signal compression. Especially, the short-term predictor is the highest complexity in MPEG-4 ALS encoder implementation because linear prediction coefficients (LPCs) for the short-term predictor are obtained from the Levinson-Durbin recursion algorithm that uses correlations of the input signals. LPCs are used for the finite impulse response (FIR) filter of the short-term predictor and its maximum coefficient order is up to 1023. Proposed is an area-efficient short-term predictor through the recursive architecture using pipeline stall and the modified radix-2 FIR algorithm is proposed. The proposed shot-term predictor has a reduction of complexity by about 95% compared to the radix-2 1024 tap FIR filter.

关键词： modified radix-2 FIR algorithm pipeline stall data compression finite impulse response filter entropy codes Levinson-Durbin recursion algorithm audio coding entropy coder FIR filter FIR filters recursive architecture audio signal compression long-term predictor area-efficient short-term predictor filtering methods in signal processing linear prediction coefficients joint coder Speech and audio coding low complexity MPEG-4 ALS encoder MPEG-4 audio lossless coding

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Adaptive grid risk-sensitive filter for non-linear problems

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IET signal processing 2011年第2期5卷 235-241页

作者： Bhaumik, S. Srinivasan, M. Sadhu, S. Ghoshal, T. K. GE India Technol Ctr JFWTC Bangalore 560066 Karnataka India Koneru Laksmaiha Coll Engn Vadeswaram AP India Jadavpur Univ Dept Elect Engn Kolkata 700032 India

A novel adaptive grid-based method has been proposed for risk-sensitive state estimation in non-linear non-Gaussian problems. The algorithm, which is based on point-mass approximation, is called the adaptive grid risk-sensitive filter (AGRSF). Although risk-sensitive estimators have been known to be robust compared to their risk-neutral counterparts, the implementation of risk-sensitive filters (RSFs) is almost impossible except for very trivial systems like linear Gaussian systems. The existing extended risk-sensitive filter (ERSF) fails to take care of non-Gaussian problems or severe non-linearities. Recently, other variants of RSFs have been proposed for extending the range of applications of risk-sensitive techniques. The AGRSF has been formulated to act as a benchmark and aid in the validation of other RSFs. The algorithm uses a modified form of information state-based recursive relation and provides guidelines for the adaptive choice of grid points to improve the numerical efficiency. The developed filter has been applied to a single-dimensional non-linear poorly observable system and a non-linear two-dimensional bearing only tracking problem. The convergence of the algorithm has been shown by simulation. The estimation efficiency and computational load of AGRSF has been compared with other RSFs.

关键词： tracking problem computational load recursive relation trivial systems nonlinear nonGaussian problems particle filtering (numerical methods) risk-sensitive state estimation risk-neutral counterparts nonlinear problems filtering methods in signal processing approximation theory Gaussian distribution tracking filters information state signal processing theory adaptive grid risk-sensitive filter adaptive Kalman filters Interpolation and function approximation (numerical analysis) point-mass approximation linear Gaussian systems nonlinear two-dimensional bearing Other topics in statistics

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Contour tracking using Gaussian particle filter

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IET IMAGE processing 2011年第5期5卷 440-447页

作者： Chen, P. Qian, H. Wang, W. Zhu, M. Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Anhui Normal Univ Coll Phys & Elect Informat Wu Hu Peoples R China

Gaussian particle filter algorithm provides a framework to estimate the state of a moving object. However, it is a known fact that parameters like noise variance and particle number affect the effectiveness of the filter greatly. To improve the performance of Gaussian particle filter in contour tracking, the authors propose a parameter adapting mechanism. To simplify the filter's implementation, a variant sampling method is also proposed. This sampling method combines sampling step with prediction step by taking advantage of the Gaussian assumption and by exploring the linear structure of the system dynamic model. Finally, comparative experiments are provided, which demonstrate the merits of the proposed algorithm.

关键词： tracking filters signal sampling contour tracking Gaussian particle filter signal processing theory state estimation prediction step Gaussian distribution Other topics in statistics noise variance filtering methods in signal processing Gaussian assumption linear structure moving object noise particle filtering (numerical methods) variant sampling sampling step

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Noise gradient reduction based on morphological dual operators

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IET IMAGE processing 2011年第1期5卷 1-17页

作者： Lei, T. Fan, Y. -Y. Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China

Noise gradient is reduced while image details gradient is also reduced by a filter. For the image corrupted by impulse noise, a novel approach of noise gradient reduction is proposed based on a pair of morphological dual operators. The noise image is filtered by a pair of morphological dual operators respectively, and then the two filtered images are provided with the complementary characteristics of the noise gradient position. This feature results from the unsymmetric behaviour of the pair of morphological dual operators, and it can be applied to reduce the noise gradient effectively. This approach is presented in detail and the experimental results show that the approach not only reduces noise gradient effectively, but also maintains image details gradient. Compared with the classical morphological dual operators, the generalised morphology dual operators have smaller root mean square error on the premise of the close computation and time.

关键词： image noise gradient reduction noise image filter image denoising Computer vision and image processing techniques mean square error methods morphological dual operators impulse noise Interpolation and function approximation (numerical analysis) filtering methods in signal processing Optical, image and video signal processing gradient methods root mean square error

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Oversampling gain in adaptive normalised matched filter detector

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IET RADAR SONAR AND NAVIGATION 2011年第9期5卷 987-993页

作者： Shi, Y. -L. Shui, P. -L. Zhao, Y. -B. Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

In radar target detection, the adaptive normalised matched filter (ANMF) detector is often used to detect moving targets in sea and land clutter environment. Its performance depends on estimation of clutter covariance matrix. The sample covariance matrix (SCM) estimator has been thoroughly analysed for independent and identically distributed Gaussian clutter vectors. Many radar systems on service use range-oversampling receivers. Referred to the range-oversampling model in the weather and oceanic radars, the spatial correlation model of range-oversampled clutter vectors is established, the error of the SCM estimator is analysed, and the range-oversampling gain, relevant to the oversampling factor and the receiver's bandwidth, is derived. The experiments using raw radar clutter data are made to verify the range-oversampling gain, showing that the range oversampling improves the performance of the ANMF detector.

关键词： meteorological radar clutter covariance matrix estimation distributed Gaussian clutter vectors sample covariance matrix estimation sea clutter environment adaptive filters land clutter environment radar imaging range-oversampled clutter vectors correlation methods Optical, image and video signal processing weather radars matched filters Algebra Radar equipment, systems and applications spatial correlation model range-oversampling gain filtering methods in signal processing covariance matrices oceanic radars raw radar clutter data moving target detection adaptive normalised matched filter detector radar clutter oversampling factor object detection radar detection radar target detection range-oversampling receivers

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Modelling of time-varying discrete-time systems

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IET signal processing 2011年第1期5卷 104-112页

作者： Wang, G. Chen, Q. Ren, Z. Chongqing Univ Posts & Telecommun Sch Commun Engn Chongqing 400065 Peoples R China

A theory to analyse linear time-varying (LTV) discrete-time systems from the point of digital signal processing of view is established here. The LTV discrete-time system is changing with time slowly in comparison with the sampling frequency of the input signal is supposed. Three models were set up to simulate and analyse the LTV system. Models 1 and 2 can be used to design and analyse the LTV system, while the filter bank representation relates the LTV system with the time-varying filter bank. Based on the established models, the input-output relationship of a time-varying system in the frequency-domain was analysed in detail.

关键词： linear time-varying discrete-time system analysis channel bank filters filtering methods in signal processing Mathematical analysis time-varying filters signal sampling discrete time filters linear systems digital signal processing time-varying filter bank signal processing theory sampling frequency LTV discrete-time system frequency-domain analysis

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Enhanced demodulation-based technique for estimating the parameters of fundamental component in power systems

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IET GENERATION TRANSMISSION & DISTRIBUTION 2011年第9期5卷 979-988页

作者： Marques, C. A. G. Ribeiro, M. V. da Silva, E. A. B. Univ Fed Rio de Janeiro Rio De Janeiro Brazil Univ Fed Juiz de Fora Juiz De Fora Brazil

The recent introduction of smart grid concepts in power systems demands metering and monitoring equipments with great deal of flexibility. To achieve this, one heavily depends upon new generation of signal processing tools. Regarding low-cost and effective signal processing-based techniques for parameters estimation in smart grids, the estimation of power components when frequency deviates from its nominal value is a timely and important issue to be addressed. The authors outline a demodulation-based technique for estimating the amplitude, phase and frequency of the fundamental component of a power line signal. This technique makes use of a finite impulse response filter design method that efficiently allocates zeros over the unit circle of the z-plane so that the performance losses yielded by power frequency deviations can be minimised if the signal is corrupted by harmonics. Numerical results indicate that the proposed technique outperforms previous ones and can be a good candidate for measurement and monitoring in flexible power systems.

关键词： z-plane smart grid power line signal enhanced demodulation finite impulse response filter design Modulation and coding methods power frequency deviations FIR filters smart power grids filtering methods in signal processing signal processing tools demodulation signal processing parameter estimation performance losses power systems Power system management, operation and economics

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Visual tracking of partially observable targets with suboptimal filtering

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IET COMPUTER VISION 2011年第1期5卷 1-13页

作者： Xu, M. Ellis, T. Godsill, S. J. Jones, G. A. Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Jiangsu Peoples R China Kingston Univ Digital Imaging Res Ctr Kingston upon Thames KT1 2EE Surrey England Univ Cambridge Dept Engn Cambridge CB2 1PZ England

A comparative study of four suboptimal tracking algorithms that can cope with missing measurements of low-level visual features during partial occlusion is presented. An approach to identify missing measurements in two-dimensional object tracking is formulated. The comparison starts from a symbolic analysis in Kalman filtering and ends with a performance evaluation in Monte Carlo simulations in which the objects manoeuvre and undergo moderate size variations during occlusion. It is found that the algorithm for estimating unobservables from observables outperforms the others in terms of mean square error, robustness and readiness for implementation.

关键词： Kalman filtering two-dimensional object tracking Kalman filters Monte Carlo simulation suboptimal filtering suboptimal tracking algorithm partial occlusion object detection Interpolation and function approximation (numerical analysis) computer vision Computer vision and image processing techniques mean square error filtering methods in signal processing Optical, image and video signal processing mean square error methods partially observable target visual tracking Monte Carlo methods symbolic analysis

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Process noise identification based particle filter: an efficient method to track highly manoeuvring targets

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IET signal processing 2011年第6期5卷 538-546页

作者： Jing, L. ChongZhao, H. Vadakkepat, P. Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

In this study, a novel method, process noise identification-based particle filter is proposed for tracking highly manoeuvring target. In the proposed method, the equivalent-noise approach is adopted, which converts the problem of manoeuvring target tracking to that of state estimation in the presence of non-stationary process noise with unknown statistics. A novel method for identifying the non-stationary process noise is proposed in the particle filter framework. Compared with the multiple model approaches for manoeuvring target tracking, the proposed method needs to know neither the possible multiple models nor the transition probability matrices. One simple dynamic model is adopted during the whole tracking process. The proposed method is especially suitable for tracking highly manoeuvring target because of its capability of dealing with sample impoverishment, which is a common problem in particle filter and becomes serious when tracking large uncertain dynamics.

关键词： transition probability matrices manoeuvring target tracking probability state estimation uncertain dynamics unknown statistics matrix algebra Other topics in statistics simple dynamic model Algebra particle filtering (numerical methods) uncertain systems equivalent-noise approach filtering methods in signal processing process noise identification based particle filter particle filter framework signal processing theory tracking process target tracking nonstationary process noise

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