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Volume 32 Issue 9
Oct.  2010
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OuYang-Cheng , Ji Hong-Bing, Zhang Jun-Gen. Improved CPHD Filter for Multitarget Tracking[J]. Journal of Electronics & Information Technology, 2010, 32(9): 2112-2118. doi: 10.3724/SP.J.1146.2009.01197
Citation: OuYang-Cheng , Ji Hong-Bing, Zhang Jun-Gen. Improved CPHD Filter for Multitarget Tracking[J]. Journal of Electronics & Information Technology, 2010, 32(9): 2112-2118. doi: 10.3724/SP.J.1146.2009.01197

Improved CPHD Filter for Multitarget Tracking

doi: 10.3724/SP.J.1146.2009.01197
  • Received Date: 2009-09-08
  • Rev Recd Date: 2009-12-29
  • Publish Date: 2010-09-19
  • The Cardinalized Probability Hypothesis Density (CPHD) fiter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. Due to the fact that there is a missed detection problem in the CPHD filter, an improved algorithm is proposed, which provides a closed-form solution under Gaussian mixture assumptions. Firstly, the estimate to track association is made by labeling each Gaussian component, and then the weights of Gaussian components having been pruned and merged are reassigned twice. At first, the Gaussian components weights exceeding the detection threshold are reassigned, which can solve the missed detection issue effectively, and then the second distribution is made based on the fact that a target can only have one measurement, which improves the performance when the targets cross each other. Simulation results show that the improved CPHD filter has advantages over the ordinary one in both the aspects of multi-target state estimation and track maintenance.
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  • Goodman I, Mahler R, and Nguyen H. Mathematics of Data Fusion [M]. Norwell, MA, Kluwer, 1997: 131-175.[2]Mahler R. Multitarget Bayes filtering via first-order multitarget moments [J].IEEE Transactions on Aerospace and Electronic Systems.2003, 39(4):1152-1178[3]Vo B N, Singh S, and Doucet A. Sequential Monte Carlo methods for Bayesian multi-target filtering with random finite sets [J].IEEE Transactions on Aerospace and Electronic Systems.2005, 41(4):1224-1245[4]Clark D, Vo B T, Vo B N, and Godsill S. Gaussian mixture implementations of probability hypothesis density filters for non-linear dynamical models [C]. IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, Birmingham, UK, April 15-16, 2008: 21-28.[5]Mahler R and Martin L. PHD filter of high order in target number [J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1523-1543.[6]Mahler R. PHD filter for nonstandard targets, Ⅱ: Unresolved targets [C]. 12th International Conference on Information Fusion, Las Vegas, NV, USA, July 6-9, 2009: 922-929.[7]Ulmke M, Franken D, and Schmidt M. Missed detection problems in the cardinalized probability hypothesis density filter [C]. 11th International Conference on Information Fusion, Cologne, Germany, June 30-July 3, 2008: 1-7.Erdinc O.[J].Willett P, and Coraluppi S. The Gaussian mixture cardinalized PHD tracker on MSTWG and SEABAR07 datasets[C]. 11th International Conference on Information Fusion, Cologne, Germany, June 30-July.2008,3:-[8]Vo B T, Vo B N, and Cantoni A. Analytic implementations of the cardinalized probability hypothesis density filter [J].IEEE Transactions on Signal Processing.2007, 55(7):3553-3567[9]Ulmke M, Erdinc O, and Willett P. Gaussian mixture cardinalized PHD filter for ground moving target tracking [C]. 10th International Conference on Information Fusion, Quebec, Que, July 9-12, 2007: 1-8.
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