KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Novel Partitioning Algorithm for a Gaussian Inverse Wishart PHD Filter for Extended Target Tracking

  • Li, Peng (Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education) ;
  • Ge, Hongwei (Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education) ;
  • Yang, Jinlong (Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education)
  • Received : 2017.04.03
  • Accepted : 2017.08.03
  • Published : 2017.11.30
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Abstract

Use of the Gaussian inverse Wishart PHD (GIW-PHD) filter has demonstrated promise as an approach to track an unknown number of extended targets. However, the partitioning approaches used in the GIW-PHD filter, such as distance partition with sub-partition (DP-SP), prediction partition (PP) and expectation maximization partition (EMP), fails to provided accurate partition results when targets are spaced closely together and performing maneuvers. In order to improve the performance of a GIW-PHD filter, this paper presents a cooperation partitioning (CP) algorithm to solve the partitioning issue when targets are spaced closely together. In the GIW-PHD filter, the DP-SP is insensitive to target maneuvers but sensitive to the differences in target sizes, while EMP is the opposite. The proposed CP algorithm is a fusion approach of DP-SP and EMP, which employs EMP as a sub-partition approach after DP. Therefore, the CP algorithm will be sensitive to neither target maneuvers nor differences in target sizes. The simulation results show that the use of the proposed CP algorithm will improve the performance of the GIW-PHD filter when targets are spaced closely together.

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References

  1. R. Mahler, "Multi-target Bayes filtering via first-order multi-target moments," IEEE Transactions on Aerospace and Electronic Systems, vol. 39, no. 4, pp. 1152-1178, April, 2003. https://doi.org/10.1109/TAES.2003.1261119
  2. B. N. Vo and W. K. Ma, "The Gaussian Mixture Probability Hypothesis Density Filter," IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4091-4104, November, 2006. https://doi.org/10.1109/TSP.2006.881190
  3. L. Gao and Y. J. Ma, "Dual Detection-Guided Newborn Target Intensity Based on Probability Hypothesis Density for Multiple Target Tracking," KSII Transactions on Internet & Information Systems, vol. 10, no. 10, pp. 5095-5111, October, 2016. https://doi.org/10.3837/tiis.2016.10.025
  4. W. Koch, "Bayesian approach to extended object and cluster tracking using random matrices," IEEE Transactions on Aerospace and Electronic Systems, vol. 44, no. 3, pp. 1042-1059, July, 2008. https://doi.org/10.1109/TAES.2008.4655362
  5. M. Feldmann and D. Franken, "Tracking of Extended Objects and Group Targets Using Random Matrices," IEEE Transactions on Signal Processing, vol. 59, no. 4, pp. 1409-1420, April, 2011. https://doi.org/10.1109/TSP.2010.2101064
  6. J. Lan, X. R. Li, "Tracking of Maneuvering Non-Ellipsoidal Extended Object or Target Group Using Random Matrix," IEEE Transactions on Signal Processing, vol. 62, no. 9, pp. 2450-2463, May, 2014. https://doi.org/10.1109/TSP.2014.2309561
  7. M. Baum and U. D. Hanebeck, "Random hypersurface models for extended object tracking," in Proc. of IEEE International Symposium on Signal Processing and Information Technology, Ajman, United arab emirates, pp.178-183, December, 2009.
  8. M. Baum and U. D. Hanebeck, "Shape tracking of extended objects and group targets with star-convex RHMs," in Proc. of 14th International Conference on Information Fusion, Chicago Illinois, USA, pp. 1-8, July, 2011.
  9. A. Zea, F. Faion, M. Baum and et al., "Level-set random hypersurface models for tracking non-convex extended objects," in Proc. of 16th International Conference on Information Fusion, Istanbul, Turkey, pp. 1760-1767, July, 2013.
  10. R. Mahler, "PHD filters for nonstandard targets, I: Extended targets," in Proc. Of 12th International Conference on Information Fusion, Seattle, WA, USA, pp. 915-921, July, 2009.
  11. K. Granstrom, C. Lundquist and U. Orguner, "A Gaussian mixture PHD filter for extended target tracking," in Proc. of 13th International Conference on Information Fusion, Edinburgh, Scotland, pp. 1-8, July, 2010.
  12. K. Granstrom, C. Lundquist and U. Orguner, "Extended target tracking using a Gaussian-mixture PHD filter," IEEE Transactions on Aerospace and Electronic Systems, vol. 48, no. 4, pp. 3268-3286, October, 2012. https://doi.org/10.1109/TAES.2012.6324703
  13. K. Granstrom and U. Orguner, "A PHD filter for tracking multiple extended targets using random matrices," IEEE Transactions on Signal Processing, vol. 60, no. 1, pp. 5657-5671, August, 2012. https://doi.org/10.1109/TSP.2012.2212888
  14. K. Granstrom and U. Orguner, "Estimation and maintenance of measurement rates for multiple extended target tracking," in Proc. of 15th Conference on Information Fusion, Singapore, pp. 2170-2176, September, 2012.
  15. K. Granstrom and U. Orguner, "On the reduction of Gaussian inverse Wishart mixtures," in Proc. of 15th Conference on Information Fusion, Singapore, pp. 2162-2169, September, 2012.
  16. K. Granstrom, A. Natale, P. Braca and et al, "Gamma Gaussian Inverse Wishart Probability Hypothesis Density for Extended Target Tracking Using X-Band Marine Radar Data," IEEE Transactions on Geoscience & Remote Sensing, vol. 53, pp. 1-15, July, 2015. https://doi.org/10.1109/TGRS.2014.2344071
  17. Y. Zhang and H. Ji, "A robust and fast partitioning algorithm for extended target tracking using a Gaussian inverse Wishart PHD filter," Knowledge-Based Systems, vol. 95, pp. 125-141, 2016. https://doi.org/10.1016/j.knosys.2015.12.008
  18. P. Li , H. W. Ge, J. L. Yang and et al., "Shape selection partitioning algorithm for Gaussian inverse Wishart probability hypothesis density filter for extended target tracking," IET Signal Processing, vol. 10, no. 9, pp. 1041-1051, October, 2016. https://doi.org/10.1049/iet-spr.2015.0503
  19. D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of Careful Seeding," in Proc. of ACM-SIAM symposium on Discrete algorithms, Philadelphi, PA, USA, pp. 1027-1035, January, 2007.
  20. Y. Han, H. Zhu and C. Z. Han, "Maintaining Track Continuity for Extended Targets Using Gaussian-Mixture Probability Hypothesis Density Filter," Mathematical Problems in Engineering, no. 3, pp. 1-16, August, 2015.
  21. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, New York, 2006.
  22. D. Schuhmacher, B. T. Vo and B. N. Vo, "A consistent metric for performance evaluation of multi-object filters," IEEE Transactions on Signal Processing, vol. 56, no. 8, pp. 3447-3457, August, 2008. https://doi.org/10.1109/TSP.2008.920469