基于标签多伯努利滤波的衍生目标跟踪算法
收稿日期: 2015-01-04
修回日期: 2015-04-12
网络出版日期: 2015-05-04
基金资助
国家"863"计划 (2014AAXXX4061)
Spawned target tracking algorithm based on labeled multi-Bernoulli filtering
Received date: 2015-01-04
Revised date: 2015-04-12
Online published: 2015-05-04
Supported by
National High-tech Research and Development Program of China (2014AAXXX4061)
针对现有随机有限集(RFS)滤波器在低信噪比环境下对衍生目标跟踪性能严重下降的问题,提出了一种基于Delta扩展标签多伯努利(δ-GLMB)滤波器的改进算法。基于随机集理论和伯努利衍生模型,推导了新的预测方程,并采用了假设裁剪及分组手段和多伯努利近似技术以降低算法的计算量。针对假设增多引起的虚警问题,将多帧平滑思想和算法相结合,利用标签信息对新目标进行回溯处理。仿真结果表明,所提算法能对目标数目进行无偏估计,在低探测概率和强杂波环境下性能明显优于概率假设密度(PHD)算法,计算开销在衍生初始阶段增长快于PHD,目标较分散时低于PHD。
邱昊 , 黄高明 , 左炜 , 高俊 . 基于标签多伯努利滤波的衍生目标跟踪算法[J]. 航空学报, 2015 , 36(9) : 3012 -3019 . DOI: 10.7527/S1000-6893.2015.0103
For the problem that the performance of existing random finite set (RFS) based filters serious degrades when tracking spawned targets in low signal-to-noise ratio condition, an improved algorithm based on δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. According to RFS theory and Bernoulli spawning model, a new predicted formulation is derived. Hypotheses pruning and grouping method along with Bernoulli approximation are adopted to cut down the computational load. To reduce the false alarms introduced by extra hypotheses, the multi-frame smooth method is employed through a new target confirmation step with label information. Simulations show that the proposed method can provide unbiased estimation of cardinality, and significant outperforms the probability hypothesis density (PHD) filter in the low detection probability and dense clutter environment. The computational cost of proposed algorithm grows faster than PHD in the early stage of spawning, while is lower than PHD when targets separate well.
[1] Mahler R. Statistical multisource multitarget information fusion[M]. Boston: Artech House, 2007: 110-120.
[2] Wu X H, Huang G M, Gao J. Particle filters for probability hypothesis density filter with the presence of unknown measurement noise covariance[J]. Chinese Journal of Aeronautics, 2013, 26(6): 1517-1523.
[3] Mahler R. Multitarget Bayes filtering via first-order multitarget moments[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152-1178.
[4] Vo B-N, Ma W K. The Gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4091-4104.
[5] Zajic T, Mahler R. A particle systems implementation of the PHD multi-target tracking filter[C]//Proceedings on Signal Processing, Sensor Fusion Target Recognition XII. Bellingham, Washington, D.C.: SPIE, 2003: 291-299.
[6] Vo B-N, Singh S, Doucet A. Sequential Monte Carlo methods for multi-target filtering with random finite sets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1224-1245.
[7] Mahler R. PHD filters of higher order in target number[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1523-1543.
[8] Vo B-T, Vo B-N, Cantoni A. Analytic implementations of the cardinalized probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3553-3567.
[9] Vo B-T, Vo B-N, Cantoni A. The cardinality balanced multi-target multi-Bernoulli filter and its implementations[J]. IEEE Transactions on Signal Processing, 2009, 57(2): 409-423.
[10] Vo B-T, Vo B-N. Labeled random finite sets and multi-object conjugate priors[J]. IEEE Transactions on Signal Processing, 2013, 61(13): 3460-3475.
[11] Vo B-N, Vo B-T, Phung D. Labeled random finite sets and the Bayes multi-target tracking filter[J]. IEEE Transactions on Signal Processing, 2014, 62(24): 6554-6567.
[12] Popp R, Pattipati K, Bar-Shalom Y. M-best SD assignment algorithm with application to multitarget tracking[C]//Proceedings on Signal and Data Processing of Small Targets. Bellingham, Washton, D.C.: SPIE, 1998: 475-495.
[13] Danchick R, Newman G E. A fast method for finding the exact N-best hypotheses for multitarget tracking[J]. IEEETransactions on Aerospace and Electronic Systems, 1993, 29(2): 555-560.
[14] Reuter S, Vo B-T, Vo B-N, et al. The labeled multi-Bernoulli filter[J]. IEEE Transactions on Signal Processing, 2014, 62(12): 3246-3260.
[15] Vo B-N, Vo B-T, Reuter S, et al. Towards large scale multi-target tracking[C]//Proceedings on Sensors and Systems for Space Applications VII. Bellingham, Washton, D.C.: SPIE, 2014: 1-6.
[16] Mahler R, Maroulas V. Tracking spawning objects[J]. IET Radar Sonar Navigation, 2013, 7(3): 321-331.
[17] Ristic B, Clark D, Vo B-N, et al. Adaptive target birth intensity for PHD and CPHD filters[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2): 1656-1668.
[18] Ristic B. Particle filters for random set models[M]. New York: Springer, 2013: 70-85.
[19] Wu X H, Huang G M, Gao J. PHD for composite tracking algorithm based on asynchronous multi-sensor multi-target measurements[J]. Acta Aeronautica et Astronautica Sinica, 2013, 34(12): 2785-2793 (in Chinese). 吴鑫辉, 黄高明, 高俊. 异步多传感器多目标PHD航迹合成算法[J]. 航空学报, 2013, 34(12): 2785-2793.
[20] Mahler R. "Statistics 102" for multisource-multitarget detection and tracking[J]. IEEE Journal of Selected Topics in Signal processing, 2013, 7(3): 376-389.
[21] Schuhmacher D, Vo B-T, Vo B-N. A consistent metric for performance evaluation of multi-object filters[J]. IEEETransactions on Signal Processing, 2008, 56(8): 3447-3457.
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