Passive multi target tracking with GM-PHD filter

This paper considers the challenging problem of multitarget tracking with passive data, obtained here by geographically distributed cameras. We use a Gaussian Mixture Probability Hypothesis Density filter approach to solve this difficult problem. As we make no spatial assumptions for the birth process, we use a slightly modified filter to obtain our results. We first describe the modified filter and detail our application before we present some results obtained on a realistic test scenario. Our simulations demonstrate that the proposed Probability Hypothesis Density filter is a promising candidate for three dimensional passive multi target tracking in clutter.

[1]  Dann Laneuville.  Bearings-only TMA on manoeuvring target: A Gaussian mixture approach , 2008 .

[2]  Yaakov Bar-Shalom,et al.  Target motion analysis in the presence of false alarms , 1993 .

[3]  Branko Ristic,et al.  Bearings-Only Tracking of Manoeuvring Targets Using Particle Filters , 2004, EURASIP J. Adv. Signal Process..

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

[5]  Ba-Ngu Vo,et al.  Closed Form PHD Filtering for Linear Jump Markov Models , 2006, 2006 9th International Conference on Information Fusion.

[6]  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.

[7]  M. Mariton,et al.  Tracking a 3D maneuvering target with passive sensors , 1991 .

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

[9]  Branko Ristic,et al.  Tracking a manoeuvring target using angle-only measurements: algorithms and performance , 2003, Signal Process..

[10]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

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

[12]  M. Vihola.  Rao-blackwellised particle filtering in random set multitarget tracking , 2007, IEEE Transactions on Aerospace and Electronic Systems.