新生目标强度未知的双门限粒子PHD滤波器
收稿日期: 2015-01-21
修回日期: 2015-04-12
网络出版日期: 2015-04-24
基金资助
国家自然科学基金(61471383);装备预研基金(9140A07030514JB14001);山东省自然科学基金(ZR2012FQ004)
A dual threshold particle PHD filter with unknown target birth intensity
Received date: 2015-01-21
Revised date: 2015-04-12
Online published: 2015-04-24
Supported by
National Natural Science Foundation of China (61471383); Equipment Pre-research Foundation (9140A07030514JB14001); Natural Science Foundation of Shandong Province (ZR2012FQ004)
传统粒子概率假设密度(PHD)滤波器假定新生目标强度已知,当新生目标在整个观测区域随机出现时不再适用。为解决新生目标强度未知时的多目标跟踪问题,提出了一种基于量测信息的双门限粒子PHD(PHD-DT)滤波器。首先基于似然函数设定门限对存活目标量测进行粗提取,利用上一时刻的目标估计值构建圆形波门进行精细提取,并对门限设定方法进行分析,然后根据提取结果对目标PHD进行分解,得到存活目标和新生目标的PHD预测及更新表达式,最后给出了滤波器的实现方法并同基于量测驱动的PHD(PHD-M)滤波器和Logic+联合概率数据互联(JPDA)方法进行了仿真对比。仿真结果表明,在新生目标强度未知时,PHD-DT可有效避免Logic+JPDA在杂波背景下因航迹起始错误带来的估计误差,并较好地解决了PHD-M的目标数目过估问题,多目标估计性能更优,且杂波越强性能优势越明显。
徐从安 , 刘瑜 , 熊伟 , 宋瑞华 , 李天梅 . 新生目标强度未知的双门限粒子PHD滤波器[J]. 航空学报, 2015 , 36(12) : 3957 -3969 . DOI: 10.7527/S1000-6893.2015.0104
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.
[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.
/
| 〈 |
|
〉 |
CJA