Particle based MAP state estimation: A comparison

MAP estimation is a good alternative to MMSE for certain applications involving nonlinear non Gaussian systems. Recently a new particle filter based MAP estimator has been derived. This new method extracts the MAP directly from the output of a running particle filter. In the recent past, a Viterbi algorithm based MAP sequence estimator has been developed. In this paper, we compare these two methods for estimating the current state and the numerical results show that the former performs better.

[1]  Hans Driessen,et al.  Particle-filter-based detection schemes , 2002, SPIE Defense + Commercial Sensing.

[2]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[3]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[4]  Hans Driessen,et al.  Mixed Labelling in Multitarget Particle Filtering , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[5]  Hans Driessen,et al.  Tracking closely spaced targets: Bayes outperformed by an approximation? , 2008, 2008 11th International Conference on Information Fusion.

[6]  Simon J. Godsill,et al.  An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo , 2007, Proceedings of the IEEE.

[7]  A. Doucet,et al.  Maximum a Posteriori Sequence Estimation Using Monte Carlo Particle Filters , 2001, Annals of the Institute of Statistical Mathematics.

[8]  Hans Driessen,et al.  The mixed labeling problem in multi target particle filtering , 2007, 2007 10th International Conference on Information Fusion.

[9]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

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

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

[12]  Rama Chellappa,et al.  Visual tracking and recognition using appearance-adaptive models in particle filters , 2004, IEEE Transactions on Image Processing.

[13]  Hans Driessen,et al.  Particle filter based sensor selection in binary sensor networks , 2008, 2008 11th International Conference on Information Fusion.

[14]  Nando de Freitas,et al.  Fast particle smoothing: if I had a million particles , 2006, ICML.

[15]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[16]  J.V. Candy,et al.  Bootstrap Particle Filtering , 2007, IEEE Signal Processing Magazine.