Sequential Monte Carlo method for the iFilter

Poisson point processes (PPP's) are very useful theoretical models for diverse applications. One of those is multi-target tracking of an unknown number of targets, leading to the intensity filter (iFilter) as a generalization of the probability hypothesis density (PHD) filter. This article develops a sequential Monte Carlo (SMC) implementation of the iFilter. In theory it was shown that the iFilter can estimate a clutter model from the measurements and thus does not need it as a-priori knowledge, like the PHD filter does. Our studies show that this property holds not only in simulations but also in real world applications. In addition it can be shown, that the performance of the PHD filter decreases substantially if the a-priori knowledge of the clutter intensity is chosen incorrectly.

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

[2]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[3]  Roy L. Streit.  Multisensor multitarget intensity filter , 2008, 2008 11th International Conference on Information Fusion.

[4]  Marek Schikora.  Global Optimal Multiple Object Detection Using the Fusion of Shape and Color Information , 2009, EMMCVPR.

[5]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

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

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

[8]  Richard F. Bass,et al.  Poisson point processes , 2011 .

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

[10]  Daniel Cremers,et al.  Multitarget, multisensor localization and tracking using passive antennas and optical sensors on UAVs , 2010, Security + Defence.

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

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

[14]  Roy L. Streit,et al.  Bayes derivation of multitarget intensity filters , 2008, 2008 11th International Conference on Information Fusion.

[15]  Daniel Cremers,et al.  Passive multi-object localization and tracking using bearing data , 2010, 2010 13th International Conference on Information Fusion.

[16]  S. Grossberg,et al.  ART 2: self-organization of stable category recognition codes for analog input patterns. , 1987, Applied optics.