新生目标强度未知的双门限粒子PHD滤波器

doi:10.7527/S1000-6893.2015.0104

Abstract

Abstract:

In situations where the targets can appear anywhere in the surveillance region, the traditional probability hypothesis density (PHD), which assumes that the target birth intensity is known a priori, is inefficient any more. To overcome this problem, a dual threshold particle PHD (PHD-DT) filter with unknown target birth intensity is proposed. First, through an analysis of threshold selection, the threshold based on likelihood function is designed for measurement crude extraction and the circular threshold based on the last state estimation is set for refined extraction. Afterwards, according to the extraction results, the predication and update equations for persistent and newborn targets are derived by the decomposition of PHD. Finally, the implementation of the PHD-DT filter is presented. The simulation results show that PHD-DT filter, which avoids the track initiation error of Logic+JPDA and succeeds in reducing the overestimate problem of PHD-M, possesses higher estimation performance. Moreover, the stronger the clutters, the more significant performance advantage of PHD-DT filter.

Key words: multi-target tracking, probability hypothesis density, unknown target birth intensity, likehood function dual threshold

CLC Number:

XU Congan, LIU Yu, XIONG Wei, SONG Ruihua, LI Tianmei. A dual threshold particle PHD filter with unknown target birth intensity[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015, 36(12): 3957-3969.

References

[1] Bar-Shalom Y, Fortman T E. Tracking and data association[M]. San Diego:Academic Press, 1988:5-40.
[2] Mahler R. Statistical multisource-multitarget information fusion[M]. Boston:Artech House Publishers, 2007:1-20.
[3] He Y, Xiu J J, Guan X. Radar data processing with applications[M]. 3rd ed. Beijing:Publishing House of Electronics Industry, 2013:1-10(in Chinese).何友,修建娟,关欣.雷达数据处理及应用[M]. 3版.北京:电子工业出版社, 2013:1-10.
[4] Mahler R. Multitarget bayes filtering via first-order multitarget moments[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4):1152-1178.
[5] Vo B N, Ma W K. The Gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2006, 54(11):4091-4104.
[6] Vo B N, Singh S, Doucet A. Sequential Monte Carlo methods for multitarget filtering with random finite sets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4):1224-1245.
[7] Clark D, Vo B T, Vo B N. Gaussian particle implementations of probability hypothesis density filters[C]//Proceedings of the 2007 IEEE Aerospace Conference. Piscataway, NJ:IEEE Press, 2007:1-11.
[8] Ou Y C, Ji H B. Weight over-estimation problem in GMP-PHD filter[J]. Electronics Letters, 2011, 47(2):139-141.
[9] Zhuang Z S, Zhang J Q, Yin J J. A kernel particle probability hypothesis density filter for multitarget tracking[J]. Acta Aeronautica et Astronautica Sinica, 2009, 30(7):1264-1270(in Chinese).庄泽森,张建秋,尹建君.多目标跟踪的核粒子概率假设密度滤波算法[J].航空学报, 2009, 30(7):1264-1270.
[10] Liu W F, Han C Z, Lian F. Multitarget state extraction for the PHD filter using MCMC approach[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 41(4):864-883.
[11] Tang X, Wei P, Chen X. Extracting targets' state from particle approximation of the PHD[J]. Journal of Electronics & Information Technology, 2010, 32(11):2091-2094(in Chinese).唐续,魏平,陈欣. PHD粒子滤波中目标状态提取方法研究[J].电子与信息学报, 2010, 32(11):2091-2094.
[12] Li W L, Jia Y M. Gaussian mixture PHD filter for jump Markov models based on best-fitting Gaussian approximation[J]. IEEE Transactions on Signal Processing, 2011, 91(4):1036-1042.
[13] Grans K, Org U. A PHD filter for tracking multiple extended targets using random matrices[J]. IEEE Transactions on Signal Processing, 2012, 60(11):5657-5671.
[14] Ma D, Fr D, Abre G T. Adaptive gating for multitarget tracking with Gaussian mixture filters[J]. IEEE Transactions on Signal Processing, 2012, 60(3):1533-1538.
[15] Michele P, Pierre D M. Mean-field PHD filters based on generalized Feynman-Kac flow[J]. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3):484-495.
[16] Ristic B, Clark D E, Vo B N. Improved SMC implementation of the PHD filter[C]//Proceedings of the 13th International Conference on Information Fusion. Piscataway, NJ:IEEE Press, 2010:1-8.
[17] Ristic B, Clark D E, Vo B N. Adaptive target birth intensity for PHD and CPHD filters[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2):1656-1668.
[18] Wang Y D, Wu J K, Ashraf A K. Data-driven probability hypothesis density filter for visual tracking[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2008, 18(8):1085-1095.
[19] Cheng T, Li X R, He Z S. Comparison of gating techniques for maneuvering target tracking in clutter[C]//Proceedings of the 17th International Conference on Information Fusion. Piscataway, NJ:IEEE Press, 2014:236-244.
[20] Schuhmacher D, Vo B T, Vo B N. A consistent metric for performance evaluation of multi-object filters[J]. IEEE Transactions on Signal Processing, 2008, 56(8):3447-3457.

A dual threshold particle PHD filter with unknown target birth intensity

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 13

Recommended Articles

Metrics

Comments

[1]	TIAN Chen, PEI Yang, HOU Peng, ZHAO Qian. Decision uncertainty based sensor management for multi-target tracking [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(10): 323781-323781.
[2]	WANG Yuebin, JIANG Jingfei, ZHANG Jianqiu. Random finite set approach to analyzing, detecting, and tracking dynamic time-frequency spectra [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(6): 322600-322600.
[3]	YAN Tao, HAN Chongzhao, ZHANG Guanghua. An overview of sensor management approaches for aerial target [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2018, 39(10): 22209-022209.
[4]	PENG Huafu, HUANG Gaoming, TIAN Wei, QIU Hao. Multi-target tracking algorithm based on amplitude and Doppler information [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2018, 39(10): 322247-322247.
[5]	YU Xiang, ZHANG Jianqiu. DOA model and tracking for Gaussian non-stationary maneuvering targets [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015, 36(10): 3430-3438.
[6]	HU Chengxiang, LIU Guixi, DONG Liang, WANG Ming, ZHANG Jingchao. Region Clutter Estimation Method for Multi-target Tracking [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2014, 35(4): 1091-1101.
[7]	WU Xinhui, HUANG Gaoming, GAO Jun. PHD for Composite Tracking Algorithm Based on Asynchronous Multi-sensor Multi-target Measurements [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(12): 2785-2793.
[8]	LUO Shaohua, XU Hui, XU Yang, AN Wei. Improved MMPHD Method for Tracking Maneuvering Targets [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2012, 33(7): 1296-1304.
[9]	SHENG Weidong, XU Dan, ZHOU Yiyu, AN Wei, LONG Yunli. Gaussian-mixture Probability Hypothesis Density Filter Based Multitarget Tracking Algorithm for Image Plane of Scanning Optical Sensor [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2011, 32(3): 497-506.
[10]	Zhou Chengxing;Liu Guixi. A Multi-target Tracking Algorithm Under Unknown Measurement Noise Distribution [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2010, 31(11): 2228-2237.
[11]	Zhuang Zesen;Zhang Jianqiu;Yin Jianjun. A Kernel Particle Probability Hypothesis Density Filter for Multi-target Tracking [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2009, 30(7): 1264-1270.
[12]	Zhuang Zesen;Zhang Jianqiu;Yin Jianjun. Rao-Blackwellized Particle Probability Hypothesis Density Filter [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2009, 30(4): 698-705.
[13]	LI Yan-jun;ZHANG Li-qun;ZHANG Ke. Research on the Multi-target Locating and Tracking Technology with the Laser Warner [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2006, 27(6): 1161-1165.