Wideband target tracking by using SVR-based sequential Monte Carlo method

In this work, a support vector regression (SVR) based sequential Monte Carlo method is presented to track wideband moving sources using a linear and passive sensor array for a signal model based on buffered data. The SVR method is employed together with a particle filter (PF) method to improve the PF tracker performance when a small sample set is available. SVR is used as a sample producing scheme for the current state vector. To provide a good approximation of the posterior density by means of improving the sample diversity, samples (particles) are drawn from an importance density function whose mean and covariance are calculated by using the pre-estimating state vector and the state vector's previous estimate. Thus, a better posterior density than the classical one can be obtained. Simulation results show that the method proposed in this work performs better than the classical one when a small sample set is available. Moreover, the results also show that a modified signal model that utilizes buffering data is superior to the signal model in Ng et al. [Application of particle filters for tracking moving receivers in wireless communication systems, in: IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Rome, Italy, June 2003, pp. 575-579].

[1]  Arnaud Doucet,et al.  Particle methods for optimal filter derivative: application to parameter estimation , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[2]  Eric Moulines,et al.  Recursive Em Algorithm with Applications to Doa Estimation , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[3]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[4]  Shahrokh Valaee,et al.  A unitary transformation algorithm for wideband array processing , 1992, [1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing.

[5]  Wen Gao,et al.  Improving particle filter with support vector regression for efficient visual tracking , 2005, IEEE International Conference on Image Processing 2005.

[6]  Simon J. Godsill,et al.  Methodology for Monte Carlo smoothing with application to time-varying autoregressions , 2000 .

[7]  Meir Feder,et al.  Recursive expectation-maximization (EM) algorithms for time-varying parameters with applications to multiple target tracking , 1999, IEEE Trans. Signal Process..

[8]  Hakan A. Çirpan,et al.  Support Vector Regression for Surveillance Purposes , 2006, MRCS.

[9]  M. Niranjan,et al.  Sequential support vector machines , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[10]  Pei-Jung Chung,et al.  Recursive EM algorithm with adaptive step size , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

[11]  Wen Gao,et al.  Automatic Multi-Player Detection and Tracking in Broadcast Sports Video using Support Vector Machine and Particle Filter , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[12]  Michael A. West,et al.  Combined Parameter and State Estimation in Simulation-Based Filtering , 2001, Sequential Monte Carlo Methods in Practice.

[13]  Asoke K. Nandi,et al.  Notes on the interpolation of discrete periodic signals using sinc function related approaches , 2000, IEEE Trans. Signal Process..

[14]  Shahrokh Valaee,et al.  Wideband array processing using a two-sided correlation transformation , 1995, IEEE Trans. Signal Process..

[15]  José Carlos Príncipe,et al.  Comments on "Sinc interpolation of discrete periodic signals" , 1998, IEEE Trans. Signal Process..

[16]  P. P. Vaidyanathan,et al.  On sampling theorems for non bandlimited signals , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[17]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[18]  Jean-Yves Tourneret,et al.  A Rao-Blackwellized particle filter for INS/GPS integration , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[19]  Hong Wang,et al.  Coherent signal-subspace processing for the detection and estimation of angles of arrival of multiple wide-band sources , 1985, IEEE Trans. Acoust. Speech Signal Process..

[20]  Mostafa Kaveh,et al.  Focussing matrices for coherent signal-subspace processing , 1988, IEEE Trans. Acoust. Speech Signal Process..

[21]  James P. Reilly,et al.  Wideband array signal processing using MCMC methods , 2003, IEEE Transactions on Signal Processing.

[22]  James P. Reilly,et al.  Application of particle filters for tracking moving receivers in wireless communication systems , 2003, 2003 4th IEEE Workshop on Signal Processing Advances in Wireless Communications - SPAWC 2003 (IEEE Cat. No.03EX689).

[23]  Christophe Andrieu,et al.  Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC , 1999, IEEE Trans. Signal Process..

[24]  A.D. Lanterman,et al.  Multiple target tracking with symmetric measurement equations using unscented Kalman and particle filters , 2004, Thirty-Sixth Southeastern Symposium on System Theory, 2004. Proceedings of the.

[25]  Thomas Schanze,et al.  Sinc interpolation of discrete periodic signals , 1995, IEEE Trans. Signal Process..

[26]  P. Laguna,et al.  Signal Processing , 2002, Yearbook of Medical Informatics.

[27]  Pei Jung Chung,et al.  Recursive EM and SAGE-inspired algorithms with application to DOA estimation , 2005, IEEE Transactions on Signal Processing.

[28]  James P. Reilly,et al.  Reversible jump MCMC for joint detection and estimation of sources in colored noise , 2002, IEEE Trans. Signal Process..

[29]  Orhan Arikan,et al.  Wideband maximum likelihood direction finding and signal parameter estimation by using the tree-structured EM algorithm , 1999, IEEE Trans. Signal Process..

[30]  William Fitzgerald,et al.  A Bayesian approach to tracking multiple targets using sensor arrays and particle filters , 2002, IEEE Trans. Signal Process..

[31]  C. Vijay Kumar,et al.  An optimal integrated tracking (ITS) for passive DOA tracking using unscented Kalman filter , 2002, Final Program and Abstracts on Information, Decision and Control.

[32]  P. P. Vaidyanathan.  SAMPLING THEOREMS FOR NON BANDLIMITED SIGNALS : THEORETICAL IMPACT AND PRACTICAL APPLICATIONS , 2001 .

[33]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[34]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .