Vol. 82

Front:[PDF file] Back:[PDF file]
Latest Volume
All Volumes
All Issues
2018-03-22

Multitarget Tracking Based on PHD Smoother with Unknown Clutter Spatial Density

By Ran Zhu, Yunli Long, and Wei An
Progress In Electromagnetics Research C, Vol. 82, 123-133, 2018
doi:10.2528/PIERC17120408

Abstract

Conventional multitarget tracking techniques assume that clutter density is known a priori and use it directly in the recursive processing. However, in practical surveillance systems, the spatial distribution density of measurements generated by clutter is unknown and time-variant. Therefore, in order to achieve better tracking performance as well as the ability to evaluate the surveillance environment, we propose a fully forward-backward probability hypothesis density (PHD) smoother integrated with clutter spatial density estimator in this paper. Details on the sequential Monte Carlo (SMC) implementation method are presented as well. Simulation results of tracking performance evaluation verify the effectiveness of the proposed PHD smoother.

Citation


Ran Zhu, Yunli Long, and Wei An, "Multitarget Tracking Based on PHD Smoother with Unknown Clutter Spatial Density," Progress In Electromagnetics Research C, Vol. 82, 123-133, 2018.
doi:10.2528/PIERC17120408
http://jpier.org/PIERC/pier.php?paper=17120408

References


    1. Mahler, R., Statistical Multisource-Multitarget Information Fusion, Artech House, Norwood, MA, 2007.

    2. Qiu, C., Z. Zhang, H. Lu, and H. Luo, "A survey of motion-based multitarget tracking methods," Progress In Electromagnetics Research B, Vol. 62, 195-223, 2015.
    doi:10.2528/PIERB15010503

    3. Mahler, R., "Multi-target Bayes filtering via first-order multi-target moments," IEEE Transactions on Aerospace and Electronic Systems, Vol. 39, No. 4, 1152-1178, 2003.
    doi:10.1109/TAES.2003.1261119

    4. Mahler, R., "PHD filters of higher order in target number," IEEE Transactions on Aerospace and Electronic Systems, Vol. 43, No. 3, 1523-1543, 2007.
    doi:10.1109/TAES.2007.4441756

    5. Qiu, C., Z. Zhang, H. Lu, and Y. Wu, "Amplitude-aided CPHD filter for multitarget tracking in infrared images," Progress In Electromagnetics Research B, Vol. 61, 211-224, 2014.
    doi:10.2528/PIERB14092101

    6. Vo, B. T., B. N. Vo, and A. Cantoni, "The cardinality balanced multitarget multi-Bernoulli filter and its implementations," EEE Transactions on Signal Processing, Vol. 57, No. 2, 409-423, 2009.
    doi:10.1109/TSP.2008.2007924

    7. Vo, B. N., S. Singh, and A. Doucet, "Sequential Monte Carlo methods for multitarget filtering with random finite sets," IEEE Transactions on Aerospace and Electronic Systems, Vol. 41, No. 4, 1224-1245, 2005.
    doi:10.1109/TAES.2005.1561884

    8. Zajic, T. and R. Mahler, "Particle-systems implementation of the PHD multitarget-tracking filter," Proceedings of SPIE --- The International Society for Optical Engineering, 291-299, Maspalomas, Spain, 2003.
    doi:10.2528/PIER11081901

    9. Hong, S., L.Wang, Z. Shi, and K. Chen, "Simplified particle PHD filter for multiple-target tracking: algorithm and architecture," Progress In Electromagnetics Research, Vol. 120, 481-498, 2011.
    doi:10.1109/TSP.2006.881190

    10. Vo, B. N. and W. K. Ma, "The Gaussian mixture probability hypothesis density filter," IEEE Transactions on Signal Processing, Vol. 54, No. 11, 4091-4104, 2006.
    doi:10.2528/PIERC15121802

    11. Gong, X., Z. Xiao, and J. Xu, "Novel multi-target tracking algorithm for automotive radar," Progress In Electromagnetics Research C, Vol. 62, 35-42, 2016.
    doi:10.2528/PIERC15121802

    12. Mahler, R., B. N. Vo, and B. T. Vo, "The forward-backward probability hypothesis density smoother," Information Fusion, Vol. 48, No. 1, 1-8, 2011.
    doi:10.1109/TAES.2012.6129665

    13. Mahler, R., B. T. Vo, and B. N. Vo, "Forward-backward probability hypothesis density smoothing," IEEE Transactions on Aerospace and Electronic Systems, Vol. 48, No. 1, 707-728, 2012.
    doi:10.1109/TAES.2012.6129665

    14. Vo, B. N., B. T. Vo, and R. Mahler, "A closed form solution to the probability hypothesis density smoother," Proceedings of IEEE International Conference on Information Fusion, 1-8, Edinburgh, UK, 2010.
    doi:10.1109/TSP.2011.2168519

    15. Vo, B. N., B. T. Vo, and R. Mahler, "Closed-form solutions to forward-backward smoothing," IEEE Transactions on Signal Processing, Vol. 60, No. 1, 2-17, 2011.
    doi:10.1109/TAES.2011.6034637

    16. Nadarajah, N., T. Kirubarajan, T. Lang, M. Mcdonald, and K. Punithakumar, "Multitarget tracking using probability hypothesis density smoothing," IEEE Transactions on Aerospace and Electronic Systems, Vol. 47, No. 4, 2344-2360, 2011.
    doi:10.1109/TAES.2011.6034637

    17. Nagappa, S. and D. E. Clark, "Fast sequential Monte Carlo PHD smoothing," Proceedings of IEEE International Conference on Information Fusion, 1-7, Chicago, USA, 2011.
    doi:10.1109/LSP.2014.2310137

    18. Wong, S., B. T. Vo, and F. Papi, "Bernoulli forward-backward smoothing for track-before-detect," IEEE Signal Processing Letters, Vol. 21, No. 6, 727-731, 2014.
    doi:10.1109/TAES.2012.6178058

    19. Chen, X., R. Tharmarasa, M. Pelletier, and T. Kirubarajan, "Integrated clutter estimation and target tracking using poisson point processes," IEEE Transactions on Aerospace and Electronic Systems, Vol. 48, No. 2, 1210-1235, 2009.
    doi:10.1109/TAES.2012.6178058

    20. Chen, X., R. Tharmarasa, T. Kirubarajan, and M. Pelletier, "Online clutter estimation using a Gaussian kernel density estimator for target tracking," Proceedings of IEEE Internati9onal Conference on Information Fusion, 1-9, Chicago, USA, 2011.
    doi:10.1049/iet-rsn.2014.0037

    21. Chen, X., R. Tharmarasa, T. Kirubarajan, and M. Mcdonald, "Online clutter estimation using a Gaussian kernel density estimator for multitarget tracking," IET Radar Sonar and Navigation, Vol. 9, No. 1, 1-9, 2015.
    doi:10.1049/iet-rsn.2014.0037

    22. Ikoma, N. and S. Godsill, "Extended object tracking with unknown association, missing observations, and clutter using particle filters," Proceedings of IEEE Workshop on Statistical Signal Processing, 502-505, St. Louis, USA, 2003.

    23. Kim, W. C., D. Musicki, T. L. Song, and J. S. Bae, "A multi scan clutter density estimator," Proceedings of IEEE International Conference on Information Fusion, 707-713, Istanbul, Turkey, 2013.

    24. Mahler, R., "CPHD and PHD filters for unknown backgrounds II: Multitarget filtering in dynamic clutter," Proceedings of International Society for Optics and Photonics In Sensors and Systems for Space Applications III, Vol. 7330, 73300L, Orlando, Florida, USA, 2009.
    doi:10.1109/TAES.2010.5595616

    25. Feng, L., C. Han, and W. Liu, "Estimating unknown clutter intensity for PHD filter," IEEE Transactions on Aerospace and Electronic Systems, Vol. 46, No. 4, 2066-2078, 2010.
    doi:10.1109/TAES.2010.5595616

    26. Li, C., Z. Jiang, B. Li, and X. Zhou, "Gaussian mixture PHD smoothing filter in unknown clutter," Journal of Xidian University, Vol. 4, 98-104, 2015.
    doi:10.1049/iet-rsn.2015.0588

    27. Shi, Y. F., S. Y. Chong, and T. L. Song, "Integrated particle smoothing for target tracking in clutter," IET Radar, Sonar & Navigation, Vol. 11, No. 4, 551-562, 2016.
    doi:10.1109/TSP.2008.920469

    28. Schuhmacher, D., 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, 3447-3457, 2008.
    doi:10.1109/TSP.2008.920469