Multi-Ships Tracking Based on Probability Hypothesis Density Filter with Unknown Birth Intensities

Recently, the developments of tracking systems have been significantly improved for ships tracking. Traditional approaches adopts a divide-and-conquer strategy, in which data association becomes quite challenging to collect right measurements in high density clutter environments. In this paper, a novel approach using Probability Hypothesis Density (PHD) filter is proposed for ships tracking, in which targets states are estimated based on set-valued measurements. Furthermore, the proposed solution also avoids the requirements of the prior parameters in the PHD filter, with respect to the birth intensities. During the tracking phase, the point matching method is also utilized to distinguish the ships and non-interested targets between consecutive frames, where the unchanged topology information is then utilized to initialize the birth intensities in the PHD filter.

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

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

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

[4]  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.

[5]  S.S. Blackman,et al.  Multiple hypothesis tracking for multiple target tracking , 2004, IEEE Aerospace and Electronic Systems Magazine.

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

[7]  Eric Mjolsness,et al.  New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence , 1998, NIPS.

[8]  Tzvetan Semerdjiev,et al.  A study of a target tracking algorithm using global nearest neighbor approach , 2003, CompSysTech '03.

[9]  Anand Rangarajan,et al.  A new point matching algorithm for non-rigid registration , 2003, Comput. Vis. Image Underst..

[10]  Xu Wenli,et al.  A geometric reasoning based algorithm for point pattern matching , 2001 .

[11]  Ronald P. S. Mahler,et al.  PHD filters for nonstandard targets, I: Extended targets , 2009, 2009 12th International Conference on Information Fusion.

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