Fast sequential Monte Carlo PHD smoothing

This paper proposes a means to achieve tractable particle PHD smoothing through the use of an augmented state space label which tracks the evolution of particles over time. The use of the label reduces the forward-backward particle smoother from quadratic to linear complexity in the number of targets allowing smoothing to be carried out on a large number of targets as well as in the presence of moderate and high levels of clutter.

[1]  Ba-Ngu Vo,et al.  Improved SMC implementation of the PHD filter , 2010, 2010 13th International Conference on Information Fusion.

[2]  Jeremie Houssineau,et al.  PHD filter with diffuse spatial prior on the birth process with applications to GM-PHD filter , 2010, 2010 13th International Conference on Information Fusion.

[3]  Ba-Ngu Vo,et al.  Forward-backward sequential Monte Carlo smoothing for joint target detection and tracking , 2009, 2009 12th International Conference on Information Fusion.

[4]  P. Fearnhead,et al.  A sequential smoothing algorithm with linear computational cost. , 2010 .

[5]  Daniel E. Clark First-moment multi-object forward-backward smoothing , 2010, 2010 13th International Conference on Information Fusion.

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

[7]  A. Doucet,et al.  Smoothing algorithms for state–space models , 2010 .

[8]  Daniel Clark Joint target-detection and tracking smoothers , 2009, Defense + Commercial Sensing.

[9]  Ba-Ngu Vo,et al.  A closed form solution to the Probability Hypothesis Density Smoother , 2010, 2010 13th International Conference on Information Fusion.

[10]  Sumeetpal S. Singh,et al.  Sequential monte carlo implementation of the phd filter for multi-target tracking , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

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

[12]  Hedvig Sidenbladh,et al.  Multi-target particle filtering for the probability hypothesis density , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

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

[14]  Kumaradevan Punithakumar,et al.  Improved multi-target tracking using probability hypothesis density smoothing , 2007, SPIE Optical Engineering + Applications.

[15]  Ba-Ngu Vo,et al.  The GM-PHD Filter Multiple Target Tracker , 2006, 2006 9th International Conference on Information Fusion.

[16]  Aurélien Garivier,et al.  Sequential Monte Carlo smoothing for general state space hidden Markov models , 2011, 1202.2945.

[17]  D.E. Clark,et al.  An Efficient Track Management Scheme for the Gaussian-Mixture Probability Hypothesis Density Tracker , 2006, 2006 Fourth International Conference on Intelligent Sensing and Information Processing.

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

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

[20]  I. R. Goodman,et al.  Mathematics of Data Fusion , 1997 .