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Particle swarm optimisation particle filtering for dual estimation

IET signal processing 2012年第2期6卷 114-121页

作者： Yang, X. Changan Univ Sch Informat Engn Xian 710064 Peoples R China

A new method for the dual estimation in dynamic state-space model was proposed in this study with a focus on sequential Bayesian learning about time-varying state and static parameter simultaneously. The proposed algorithm combines auxiliary particle filtering (APF) with particle swarm optimisation (PSO) to achieve computational efficiency and stability. The PSO provides the mechanism for generating new parameter values for the particle filtering at each time step. By properly choosing the fitness function of PSO, the algorithm produces the recursive maximum-likelihood estimation of the parameter. It is shown that PSO can be integrated with APF in the simulation-based sequential frame for dual estimation. The algorithm is tested on Markov switching stochastic volatility model with promising results compared with existing ones.

关键词： particle swarm optimisation auxiliary particle filtering time-varying state recursive estimation learning (artificial intelligence) Optimisation techniques simulation-based sequential frame recursive maximum-likelihood estimation PSO fitness function maximum likelihood estimation dual estimation particle filtering (numerical methods) dynamic state-space model filtering methods in signal processing Bayes methods sequential Bayesian learning signal processing theory Markov switching stochastic volatility model static parameter Markov processes sequential estimation

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Adaptive filtering in phase space for foetal electrocardiogram estimation from an abdominal electrocardiogram signal and a thoracic electrocardiogram signal

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IET signal processing 2012年第3期6卷 171-177页

作者： Wei, Z. Hongxing, L. Jianchun, C. Nanjing Univ Sch Elect Sci & Engn Nanjing Jiangsu Peoples R China Jiangsu Univ Sci & Technol Sch Elect Informat Zhenjiang Peoples R China

In this study, an adaptive filtering in phase space for foetal electrocardiogram (ECG) extraction from a maternal abdominal ECG signal and a thoracic ECG signal is proposed. The phase space trajectory is reconstructed with one abdominal ECG signal and one thoracic ECG signal from a pregnant women. The non-linear and time-variant relationship between the thoracic ECG signal and maternal ECG component in abdominal signal is built by the adaptive local modelling in phase space, and the maternal ECG component in abdominal ECG signal can be estimated, which is then subtracted from the abdominal ECG signal to obtain a foetal ECG estimate. The local modelling process is carried on the spatial neighbouring points of each point in phase space, not on the temporal neighbouring points of each point in time sequence as usual. This can utilise the geometric information of the periodic ECG signal, and the simpler local relationship represented in phase space makes the estimation of the relationship easier and more accurate. Compared with the classical adaptive filter, the proposed method has shown the improved robustness and fidelity in restoration of the foetal ECG, testing with synthetic and real ECG signals.

关键词： electrocardiography Biology and medical computing adaptive filters deconvolution foetal ECG extraction phase space adaptive filtering Digital filters Bioelectric signals abdominal electrocardiogram signal maternal thoracic ECG signal Digital signal processing Electrical activity in neurophysiological processes maternal abdominal ECG signal adaptive local modelling local modelling process obstetrics filtering methods in signal processing foetal electrocardiogram estimation Electrodiagnostics and other electrical measurement techniques medical signal processing periodic ECG signal geometric information thoracic electrocardiogram signal phase space trajectory reconstruction

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Estimators for autoregressive moving average signals with multiple sensors of different missing measurement rates

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IET signal processing 2012年第3期6卷 178-185页

作者： Sun, S. L. Li, X. Y. Yan, S. W. Heilongjiang Univ Dept Automat Harbin 150080 Peoples R China

This study is concerned with the optimal linear estimation problems for multi-sensor autoregressive moving average (ARMA) signals with missing measurements, which can be converted into estimation problems of the state and white noise in the state space representation. The missing measurements from different sensors are described by a group of Bernoulli distributed random variables. Using the projection theory, the optimal linear estimators including filter, predictor and smoother for the state and white noise are derived in the linear minimum variance sense. Furthermore, the centralised optimal estimators for ARMA signals with multiple sensors of different missing measurement rates are obtained. The previous estimation algorithms under complete measurement data in references have lost the optimality when there are missing measurements of sensors. At last, the stability of the proposed estimators is analysed. Simulation results show the effectiveness of the proposed optimal linear estimators.

关键词： measurement data ARMA signals projection theory smoother white noise missing measurement rates Sensor fusion autoregressive moving average processes optimal linear estimation problems multiple sensors predictor state space representation filtering methods in signal processing Bernoulli distributed random variables linear minimum variance sense estimation theory multisensor autoregressive moving average signal estimation signal processing theory smoothing methods sensor fusion filter Other topics in statistics

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Oblique projection polarisation filtering for interference suppression in high-frequency surface wave radar

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IET RADAR SONAR AND NAVIGATION 2012年第2期6卷 71-80页

作者： Mao, X. -P. Liu, A. -J. Hou, H. -J. Hong, H. Guo, R. Deng, W. -B. Harbin Inst Technol Harbin 150001 Peoples R China

Polarisation filtering is a valid approach for interference suppression in high-frequency surface wave radar (HFSWR) and other systems. Based on the fundamental principle of the oblique projection and polarised filtering, an oblique projection polarisation filter (OPPF), which can be constructed from the polarisation subspaces of the target signal and those of the interference or directly from experimental data, is proposed in this study. Generalised methods for constructing the OPPF operators (theoretical OPPF and improved OPPF) are provided and the impact on the performance caused by the estimation errors is also discussed. Numerical results from simulation and experimental data demonstrate that the proposed filter is an effective means of interference cancellation. It is proved that OPPF is an extension of the conventional polarised filter, whereas the improved OPPF is more suitable for the situation where the interference is unknown.

关键词： polarisation subspaces Electromagnetic compatibility and interference filtering methods in signal processing generalised methods estimation errors Radar equipment, systems and applications target signal oblique projection polarisation filtering radar applications filtering theory Other numerical methods numerical analysis high-frequency surface wave radar interference suppression

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Detection of human presence in a vehicle by vibration analysis

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IET INTELLIGENT TRANSPORT SYSTEMS 2012年第4期6卷 413-420页

作者： Ranta, R. Decoster, Y. Orlewski, P. Univ Lorraine CNRS CRAN F-54516 Vandoeuvre Les Nancy France IEE L-5326 Luxembourg Luxembourg

The aim of this study is to propose a complete instrumentation and signal processing method able to detect the presence of a person seated on the rear bench of a vehicle. The sensor is based on a piezoelectric film (EMFI sensor), designed to detect mechanical vibrations. In order to avoid confusion between humans and heavy objects or empty seats, the authors focused on the extraction of a biological signature from the acquired signals. This physiological pattern was extracted using an original wavelet denoising algorithm and was used further as a matched filter, in order to detect human presence in the vibration signals. Physiologically significant features were extracted from the output of the (on-line) filtering process and fed further-on into a classical Bayes-based classifier. After training, the proposed method yielded very promising results, the output of the classifier being almost error-free for different acquisition conditions (stopped and on-road vehicle, new and artificially aged sensor).

关键词： Mechanical variables measurement Automobile electronics signal denoising mechanical vibration detection filtering methods in signal processing signal processing method vehicle rear bench thin film sensors vibration measurement piezoelectric devices biological signature Bayes based classifier matched filters EMFI sensor piezoelectric film sensor Bayes methods human presence detection wavelet denoising algorithm vibration analysis complete instrumentation Sensing devices and transducers automotive electronics matched filter Piezoelectric devices Other topics in statistics

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Subspace-based blind channel estimation in nearly saturated down link multicarrier code division multiple access systems

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IET COMMUNICATIONS 2012年第4期6卷 408-412页

作者： Al-Bayati, A. K. S. Hashemite Univ Dept Elect Engn Zarqa Jordan

A new subspace-based blind method for channel estimation in nearly saturated downlink multicarrier code division multiple access systems is proposed. In this method, the authors exploit the subspace structure of the received signal to estimate the channel directly in the frequency domain irrespective of the length of the finite impulse response (FIR) channel, thus allowing operation in nearly saturated systems (number of users can be one less than process gain). The method is shown to outperform some earlier blind channel estimation methods, whereas at the same time avoiding the limitations in the number of users that these methods suffer from.

关键词： filtering methods in signal processing code division multiple access subspace structure Mathematical analysis Radio links and equipment Communication channel equalisation and identification finite impulse response channel FIR filters nearly saturated down link multicarrier code division multiple access systems frequency-domain analysis channel estimation FIR channel frequency domain Multiple access communication subspace-based blind channel estimation method

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Efficient multisensor fusion with sliding window Kalman filtering for discrete-time uncertain systems with delays

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IET signal processing 2012年第5期6卷 446-455页

作者： Song, I. Y. Jeon, M. Shin, V. Gwangju Inst Sci & Technol Sch Informat & Mechatron Kwangju 500712 South Korea Gyeongsang Natl Univ Dept Informat & Stat Jinju 660701 Gyeongsangnam D South Korea Gyeongsang Natl Univ RINS Jinju 660701 Gyeongsangnam D South Korea

In this study, we provide two computationally effective multisensory fusion filtering algorithms for discrete-time linear uncertain systems with state and observation time-delays. The first algorithm is shaped by algebraic forms for multirate sensor systems, and then we propose a matrix form of filtering equations using block matrices. The second algorithm is based on exact cross-covariance matrix equations. These equations are useful to compute matrix weights for fusion estimation in a multidimensional-multisensor environment. Furthermore, our proposed filtering algorithms are based on the sliding window strategy in order to achieve high estimation accuracy and stability under parametric uncertainties. The authors demonstrate the low computational complexities of the proposed fusion filtering algorithms and how the proposed algorithms robust against dynamic model uncertainties comparing with Kalman filtering with time delays.

关键词： fusion estimation matrix form computationally effective multisensory fusion filtering algorithms exact cross-covariance matrix equations sliding window Kalman filtering state time-delays matrix weights Distributed parameter control systems filtering equations observation time-delays multidimensional systems algebraic forms multisensor fusion multidimensional-multisensor environment dynamic model uncertainty Algebra uncertain systems delays stability filtering methods in signal processing covariance matrices multirate sensor systems estimation theory Kalman filters Discrete control systems signal processing theory discrete-time uncertain systems discrete time systems block matrices computational complexity estimation accuracy sensor fusion sliding window strategy parametric uncertainty Other topics in statistics

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Characterisation of tool marks on cartridge cases by combining multiple images

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IET IMAGE processing 2012年第7期6卷 854-862页

作者： Leloglu, U. M. TUBITAK UZAY TR-06531 Ankara Turkey

The characteristic marks left by firearms on cartridge cases (CCs) during firing are used by forensic experts to identify CCs fired from the same firearm;however, the nature of the tool marks on the CCs is not well understood. The objective of this study is to separate the tool marks i.e., the signal, from the background signal and the noise and thereby understand its peculiarities. To extract the signal, which is much weaker than the noise, second-order derivatives of three-dimensional images of the surfaces of a series of CCs fired from the same firearm were used. Instead of using rigid body transformation, the images are first registered based on estimated planar homographies;then unwanted areas, including the headstamp are masked out. Aligned images are merged by weighted averaging using estimated signal and noise variances, which are also used for calculating signal and noise spectra. Wiener filtering is applied to further increase the signal-to-noise ratio. The effectiveness of the method is demonstrated on real data. It is also shown that breech face marks exist on the outer ring, a feature typically ignored in automatic matching. In addition, visual results obtained by reconstructing the surface from second-order derivatives are presented.

关键词： forensic experts filtering methods in signal processing Military circuits, components, and equipment firearm second order derivatives Computer vision and image processing techniques image processing planar homographies estimation Optical, image and video signal processing Wiener filters weapons noise variances three-dimensional images background signal signal-to-noise ratio CC automatic matching law Military engineering computing tool mark characterisation noise spectra signal extraction Wiener filtering military computing cartridge cases

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Cognition-based parameter setting of non-linear filters using a face recognition system

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IET IMAGE processing 2012年第8期6卷 1057-1063页

作者： Matsumoto, M. Univ Electrocommun Educ & Res Ctr Frontier Sci Chofu Tokyo 1828585 Japan

This study introduces cognition-based parameter setting of non-linear filters using a face recognition system. The parameter setting problem is generally solved by the maximisation or minimisation of some objective evaluation functions such as correlation and statistical independence. However, the tuned filter output is not always adequate for face recognition systems because filter and system are separately tuned using different criterion. When a single image with noise is considered, it is difficult to employ such objective functions because only a single image exists, and the original image cannot be known. When the authors consider some problems such as contrast adjustment and non-photo-realistic rendering, it is also difficult to set an objective criterion for parameter setting because there are no correct solutions other than humans' subjectivity. To handle such cases, the authors look to some subjective information such as a face in an image, and directly employ a face recognition system as the evaluation function for parameter setting. Experimental results show that cognition-based evaluation has the potential to handle these types of problems.

关键词： face recognition system rendering (computer graphics) objective evaluation function maximisation nonlinear filters Image recognition face recognition Computer vision and image processing techniques Graphics techniques contrast adjustment objective evaluation function minimisation minimisation nonlinear filter filtering methods in signal processing cognition-based parameter setting Optimisation techniques nonphoto-realistic rendering

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Oriented diffusion filtering for enhancing low-quality fingerprint images

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IET BIOMETRICS 2012年第2期1卷 105-113页

作者： Gottschlich, C. Schoenlieb, C. -B. Univ Gottingen Inst Math Stochast D-37077 Gottingen Germany Univ Cambridge Dept Appl Math & Theoret Phys Cambridge CB3 0WA England

To enhance low-quality fingerprint images, we present a novel method that first estimates the local orientation of the fingerprint ridge and valley flow and next performs oriented diffusion filtering, followed by a locally adaptive contrast enhancement step. By applying the authors' new approach to low-quality images of the FVC2004 fingerprint databases, the authors are able to show its competitiveness with other state-of-the-art enhancement methods for fingerprints like curved Gabor filtering. A major advantage of oriented diffusion filtering over those is its computational efficiency. Combining oriented diffusion filtering with curved Gabor filters led to additional improvements and, to the best of the authors' knowledge, the lowest equal error rates achieved so far using MINDTCT and BOZORTH3 on the FVC2004 databases. The recognition performance and the computational efficiency of the method suggest to include oriented diffusion filtering as a standard image enhancement add-on module for real-time fingerprint recognition systems. In order to facilitate the reproduction of these results, an implementation of the oriented diffusion filtering for Matlab and GNU Octave is made available for download.

关键词： mathematics computing fingerprint identification Image recognition GNU Octave visual databases low-quality fingerprint image enhancement fingerprint ridge MINDTCT curved Gabor filtering oriented diffusion filtering Computer vision and image processing techniques filtering methods in signal processing image enhancement FVC2004 fingerprint databases standard image enhancement add-on module locally adaptive contrast enhancement step valley flow Gabor filters BOZORTH3 Matlab real-time fingerprint recognition systems

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