A sequential Monte Carlo method for PHD approximation with conditionally linear/Gaussian models

A new sequential Monte Carlo procedure for approximating the probability hypothesis density is proposed. The algorithm, based on the replacement of numerical approximation with exact computation, is applicable to the class of conditionally linear/Gaussian models. The proposed algorithm is applied with an efficient, measurement-directed importance density to multiple target tracking using range-bearings measurements. Performance for a given sample size is significantly better than the previously proposed SMC-PHD.

[1]  S. Godsill,et al.  Auxiliary Particle Implementation of the Probability Hypothesis Density Filter , 2007, 2007 5th International Symposium on Image and Signal Processing and Analysis.

[2]  Ronald P. S. Mahler,et al.  Statistical Multisource-Multitarget Information Fusion , 2007 .

[3]  Ronald P. S. Mahler,et al.  Particle-systems implementation of the PHD multitarget-tracking filter , 2003, SPIE Defense + Commercial Sensing.

[4]  A. Doucet,et al.  Sequential Monte Carlo methods for multitarget filtering with random finite sets , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[5]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[6]  R. Mahler.  Multitarget Bayes filtering via first-order multitarget moments , 2003 .

[7]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[8]  Hedvig Kjellström,et al.  Multi-target particle filtering for the probability hypothesis density , 2003, ArXiv.

[9]  R. Mahler,et al.  PHD filters of higher order in target number , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Ba-Ngu Vo,et al.  The Gaussian Mixture Probability Hypothesis Density Filter , 2006, IEEE Transactions on Signal Processing.

[11]  Sumeetpal S. Singh,et al.  Convergence of the SMC Implementation of the PHD Filte , 2006 .

[12]  Ba Tuong Vo,et al.  Random finite sets in Multi-object filtering , 2008 .

[13]  Y. Ho,et al.  A Bayesian approach to problems in stochastic estimation and control , 1964 .

[14]  Ba-Ngu Vo,et al.  Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter , 2007, IEEE Transactions on Signal Processing.

[15]  Daniel E. Clark,et al.  Convergence results for the particle PHD filter , 2006, IEEE Transactions on Signal Processing.

[16]  G. Casella,et al.  Rao-Blackwellisation of sampling schemes , 1996 .

[17]  Ba-Ngu Vo,et al.  A Consistent Metric for Performance Evaluation of Multi-Object Filters , 2008, IEEE Transactions on Signal Processing.