Extended Target Tracking in Presence of Target Crossing

doi:10.16182/j.issn1004731x.joss.201701002

Abstract

Abstract: In the process of extended target tracking, if there are some crossing targets, target missing problem in target estimation will appear when the extended target Gaussian mixture probability hypothesis density filter is used directly. Aiming at this problem, an improved algorithm was proposed. Euclidean distances among targets estimated were calculated at each time, and then these targets were distinguished if they were in an adjacent region or not. In the next time step, Gaussian components weights of the targets in the same adjacent region were compensated if the target number estimated became small; otherwise, there was nothing to do. These Gaussian components were applied to estimate and track the targets. The improved algorithm solved the estimated target missing problem when the extended targets were so close with partitioning into the same subset. Simulation results show that the improved algorithm is accuracy and effective.

Key words: extended target tracking, probability hypothesis density filter, random finite sets, sate estimation, target crossing

CLC Number:

TP391.9

Chen Jinguang, Jiang Mengxi, Ma Lili. Extended Target Tracking in Presence of Target Crossing[J]. Journal of System Simulation, 2017, 29(1): 7-13.

References

[1] Vo B N, Singh S, Doueet A.Sequential Monte Carlo methods for multi-target filtering with random finite sets[J]. IEEE Transactions on Aerospace and Electronic Systems (S0018-9251), 2005, 41(4): 1224-1245.
[2] Vo B N, Ma W.The Gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Signal Processing (S1053-587X), 2006, 54(11): 4091-4104.
[3] Gilholm K, Salmond D.Spatial distribution model for tracking extended objects[J]. IEE Proceedings-Radar, Sonar and Navigation (S1350-2395), 2005, 152(5): 364-371.
[4] K Gilholm, S Godsill, S Maskell, et al.Poisson models for extended target and group tracking[C]// Proceedings of Signal and Data Processing of Small Targets. USA: SPIE, 2005: 230-241.
[5] Mahler R P S. PHD filters for nonstandard targets. I: Extended targets[C]// Proceedings of the 12th International Conference on Information Fusion. USA: IEEE, 2009: 915-921.
[6] Granstrom K, Lundquist C, Orguner O.Extended target tracking using a Gaussian-mixture PHD filter[J]. IEEE Transactions on Aerospace and Electronic Systems (S0018-9251), 2012, 48(4): 3268-3286.
[7] Orguner O, Lundquist C, Granstrom K.Extended target tracking with a cardinalized probability hypothesis density filter[C]// Proceedings of the 14th International Conference on Information Fusion. USA: IEEE, 2011: 1-8.
[8] Angelova D, Mihaylova L.Extended object tracking using Monte Carlo methods[J]. IEEE Transactions on Signal Processing (S1053-587X), 2008, 56(2): 825-832.
[9] Guanghua Zhang, Feng Lian, Chongzhao Han.CBMeMBer filters for nonstandard targets, I: Extended targets[C]// Proceedings of the 17th International Conference on Information Fusion. Spain: IEEE, 2014: 1-6.
[10] Daniel Sigalov, Tomer Michaeli, Yaakov Oshman.Simultaneous tracking and data association in an extended maneuvering target using the IMM methodology[C]// Proceedings of the 17th International Conference on Information Fusion. Spain: IEEE, 2014: 1-7.
[11] Amadou Gning, Simon J Julier, Jordi Barr, et al.PHD filtering in presence of highly structured sea clutter process and tracks with extent[C]// Proceedings of the IET Conference on Data Fusion & Target Tracking: Algorithms and Applications. UK: IET, 2014: 1-8.
[12] Jinguang Chen, Ni Wang, Lili Ma, et al.Extended target probability hypothesis density filter based on cubature Kalman filter[J]. IET Radar, Sonar and Navigation (S1751-8784), 2015, 9(3): 324-332.
[13] Xu Tang, Xin Chen.A multiple-detection probability hypothesis density filter[J]. IEEE Transactions on Signal Processing (S1053-587X), 2015, 63(8): 2007-2019.
[14] Koch J W.Bayesian approach to extended object and cluster tracking using random matrices[J]. IEEE Transactions on Aerospace and Electronic Systems (S0018-9251), 2008, 44(3): 1042-1059.
[15] Schuhmacher Dominic, B T Vo, B N Vo. A consistent metric for performance evaluation of multi-object filters[J]. IEEE Transactions on Signal Processing (S1053-587X), 2004, 56(8): 3447-3457.