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Fixed-point design of generalised comb filters: a statistical approach

IET signal processing 2010年第2期4卷 158-167页

作者： Laddomada, M. Texas A&M Univ Dept Elect Engn Texarkana TX USA

This study is concerned with the problem of designing computationally efficient generalised comb (GC) filters. Basically, GC filters are anti-aliasing filters that guarantee superior performance in terms of selectivity and quantisation noise rejection compared to classical comb filters, when used as decimation filters in multistage architectures. Upon employing a partial polyphase (PP) architecture proposed in a companion study, the authors develop a sensitivity analysis in order to investigate the effects of the coefficients' quantisation on the frequency response of the designed filters. The authors show that the sensitivity of the filter response to errors in the coefficients is dependent on the particular split of the decimation factor between the two sub-filters constituting the PP architecture. The sensitivity analysis is then used for developing a fixed-point implementation of a sample filter from the class of GC filters, used as reference filter throughout the study. Finally, the authors present computer simulations in order to evaluate the performance of the designed fixed-point filters.

关键词： statistical analysis multistage architecture decimation filter quantisation coefficient partial polyphase architecture reference filter statistical approach quantisation (signal) generalised comb filters comb filters filtering methods in signal processing antialiasing filter quantisation noise rejection signal processing theory sensitivity analysis fixed-point design filter response computer simulations Other topics in statistics

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Family of affine projection adaptive filters with selective partial updates and selective regressors

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IET signal processing 2010年第5期4卷 567-575页

作者： Abadi, M. S. E. Mehrdad, V. Shahid Rajaee Teacher Training Univ Fac Elect & Comp Engn Tehran Iran

In this study the concepts of selective partial updates (SPU) and selective regressors (SR) in the affine projection adaptive filtering algorithm are combined and the family of affine projection algorithms (APAs) with SPU and SR features are established. These algorithms are computationally efficient. The mean-square performance of the presented algorithms are analysed based on the energy conservation arguments of Sayed's group. This analysis does not need to assume a Gaussian or white distribution for the regressors. The authors demonstrate the performance of the presented algorithms through simulations. The good agreement between theoretically predicted and actually observed performances is also demonstrated.

关键词： Integral transforms selective regressors filtering methods in signal processing SR adaptive filtering algorithm adaptive filters regression analysis energy conservation SPU mean-square performance selective partial updates mean square error methods affine projection algorithms affine transforms Other topics in statistics

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Adaptive non-linear filter using a modular polynomial perceptron

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IET signal processing 2010年第6期4卷 640-649页

作者： Zhao, H. Q. Zeng, X. P. Zhang, J. S. Liu, Y. G. Li, T. R. SW Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China SW Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Peoples R China Zhejiang Univ Ningbo Inst Technol Ningbo 315100 Zhejiang Peoples R China

This study presents a joint adaptive non-linear filter with pipelined second-order polynomial perceptron (PSOVNN) to reduce the computational complexity and improve the non-linear processing capability of adaptive direct-form second-order Volterra (SOV) filter. The PSOVNN is a nesting modular structure comprising a number of modules that are interconnected in a chained form. Each module is implemented by a small-scale direct-form SOV neural network (SOVNN). These cascaded modules can perform a non-linear mapping from the input space to an intermediate space. In addition, the linear filter of the complete PSOVNN performs a linear mapping from the intermediate space to the output space. A modified real-time recurrent learning (RTRL) algorithm is developed, and its performance is evaluated by a series of simulation experiments. Computer simulations indicate that the proposed non-linear filter exhibits better performance over the direct-form SOV filter with less computational complexity.

关键词： nonlinear filters adaptive nonlinear filter filtering methods in signal processing polynomials signal processing theory pipelined second-order polynomial perceptron computational complexity adaptive filters linear mapping Algebra second-order Volterra filter SOV neural network real-time recurrent learning algorithm

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Design of reduced-order l₂-l_∞ filter design for singular discrete-time systems using strict linear matrix inequalities

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IET CONTROL THEORY AND APPLICATIONS 2010年第3期4卷 509-519页

作者： Mahmoud, M. S. Xia, Y. King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran 31261 Saudi Arabia Beijing Inst Technol Dept Automat Control Beijing 100081 Peoples R China

In this study, the problem of designing regular robust l(2)-l(infinity) filter for a class of linear singular discrete-time systems with norm-bounded uncertainties is investigated. A class of regular linear filters is fully analysed, then necessary and sufficient conditions of robust admissibility are established. Guaranteed l(2)-l(infinity) performance of the filtered system is provided and expressed in terms of the solution of strict linear matrix inequalities (LMIs). The cases of full- and zeroth-order realisable filter are derived as limiting cases of design. Numerical examples are worked out to illustrate the theoretical development.

关键词： signal processing theory filtering theory Linear algebra (numerical analysis) strict linear matrix inequality l∞ filter design reduced order l2 filter linear singular discrete time systems norm bounded uncertainties discrete time systems filtering methods in signal processing linear matrix inequalities zeroth order filter

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Kalman filtering for continuous-time systems with time-varying delay

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IET CONTROL THEORY AND APPLICATIONS 2010年第4期4卷 590-600页

作者： Wang, W. Zhang, H. Xie, L. Harbin Inst Technol Shenzhen Grad Sch Shenzhen Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

The optimal filtering problem for continuous-time linear systems with time-varying delay is considered. The purpose is to find a Riccati equation-based solution to the linear minimum mean square error filter. The time-varying delayed observation is firstly rewritten as an equivalent observation with multiple constant delays by defining a binary variable to model the arrival process of the observation. Then, by constructing certain multiple channel observations sequence that contains the same amount of information as the original one and applying the re-organised innovation analysis approach, the proposed problem is transformed to performing standard time-varying Kalman filtering.

关键词： signal processing theory least mean squares methods Kalman filters Riccati equation time-varying systems Time-varying control systems re-organised innovation analysis approach linear systems time-varying filters linear minimum mean square error filter delays continuous time filters time-varying Kalman filtering Mathematical analysis time-varying delay filtering methods in signal processing binary variable continuous time linear systems multiple channel observations sequence Riccati equations Distributed parameter control systems optimal filtering problem

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Adaptive sensor selection in wireless sensor networks for target tracking

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IET signal processing 2010年第5期4卷 530-536页

作者： Zoghi, M. Kahaei, M. H. Iran Univ Sci & Technol Sch Elect Engn Tehran Iran

Sensor selection in wireless sensor networks (WSN) is considered for target tracking. A decentralised estimation scenario is considered based on the extended information filter. A cost function using the geometrical dilution of precision (GDOP) measure is derived for active sensor selection. Accordingly, a new adaptive sensor selection (ASS) algorithm is proposed in which the number of active sensors is adaptively determined based on the absolute local innovations vector. Simulation results show that the tracking accuracy of the ASS is comparable to that of the other algorithms, but with less energy consumption.

关键词： adaptive sensor selection absolute local innovations vector extended information filter filtering methods in signal processing Wireless sensor networks geometrical dilution of precision decentralised estimation sensor placement target tracking Kalman filters wireless sensor networks active sensors

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Multihypothesis recursive video denoising based on separation of motion state

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IET IMAGE processing 2010年第4期4卷 261-268页

作者： Tan, H. Tian, F. Qiu, Y. Wang, S. Zhang, J. Chongqing Univ Coll Commun Engn Chongqing 400044 Peoples R China Chongqing Commun Inst Chongqing 400035 Peoples R China

A multihypothesis recursive video denoising filter (MRF) based on separation of motion state is proposed. For video sequence degraded by additive Gaussian white noise, local motion state will be detected combining multiple hypotheses (temporal predictions) first. Then different denoising schemes will be selected to suppress the noise according to the local motion state. Areas detected as stationary motion will be filtered by multihypothesis motion compensated filter (MHMCF), whereas areas detected as non-stationary motion will be filtered by self-cross-bilateral filter (SCBF). The definitions of stationary motion state and non-stationary motion state are given. In addition, the threshold used to classify motion state is equal to the noise standard deviation. The simulation results show that MRF outperforms conventional denoising methods like joint filtering scheme, spatio-temporal varying filter and MHMCF both in peak signal-to-noise ratio and visual quality.

关键词： spatio-temporal varying filter additive Gaussian white noise filtering methods in signal processing image motion analysis video signal processing video sequence local motion state Optical, image and video signal processing peak signal-to-noise ratio visual quality stationary motion image sequences multihypothesis recursive video denoising filter recursive filters filtering theory Video signal processing joint filtering scheme image denoising self-cross-bilateral filter nonstationary motion

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Square-root second-order extended Kalman filter and its application in target motion analysis

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IET RADAR SONAR AND NAVIGATION 2010年第3期4卷 329-335页

作者： Daowang, F. Teng, L. Tao, H. Z. Natl Univ Def Technol China Dept Elect Sci & Engn Changsha 410073 Hunan Peoples R China

In this study, a new non-linear filter based on second-order extended Kalman technology and square-root Kalman algorithm is proposed and applied to the problem of target motion analysis (TMA). By orthogonalising the state vectors with the square root of covariance matrix, the authors reduce the computational complexity in the second-order extended Kalman algorithm, and then we deduce a second-order extended Kalman algorithm based on square-root Kalman technology, which is more numerically stable and can guarantee positive and semi-definiteness of the state covariance. The authors apply the new algorithm to a TMA problem, and prove with both numerical simulation and actual experimentation that their algorithm performs well in the significant non-linear systems.

关键词： nonlinear filters Digital signal processing covariance matrix filtering methods in signal processing covariance matrices motion estimation target motion analysis nonlinear filter state covariance state vectors TMA second order extended Kalman algorithm target tracking Kalman filters square root Kalman algorithm Algebra nonlinear systems

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Comment on 'Huber-based unscented filtering and its application to vision-based relative navigation'

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IET RADAR SONAR AND NAVIGATION 2010年第5期4卷 744-745页

作者： Karlgaard, C. D. Schaub, H. Analyt Mech Associates Inc Hampton VA USA Univ Colorado Dept Aerosp Engn Sci Boulder CO 80309 USA

In a recent paper of the publication, Wang et al. develop a robust sigma-point Kalman filter using Huber's generalised maximum likelihood estimation technique. The wording and development of Wang et al. seems to be taken directly from two papers authored by Karlgaard and Karlgaard and Schaub. Some discussions in the second to last paragraph in the Introduction of Wang et al. matches with passages from Karlgaard and Karlgaard and Schaub and Section 2.2 of Wang et al. is almost directly taken from Karlgaard. The mathematical symbols are different in places but the overall flow and wording of the section is nearly identical to that in Karlgaard. Reference Karlgaard is included in their references but it was not cited in Section 2.2 as the basis for their formulation, and Section 2.2 is almost word for word from that paper, starting with (14). It should be pointed out that there seems to be a typographical error.

关键词： filtering methods in signal processing navigation Kalman filters Radionavigation and direction finding vision-based relative navigation Huber-based unscented filtering

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Comment on 'Huber-based unscented filtering and its application to vision-based relative navigation' Reply

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IET RADAR SONAR AND NAVIGATION 2010年第5期4卷 746-746页

作者： Wang, X. Harbin Inst Technol Harbin 150006 Peoples R China

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