-
摘要: 针对多机动目标追踪问题, 将交互式多模型(Interacting multiple model, IMM)思想与箱粒子标签多伯努利滤波器(Box-labeled multi-bernoulli filter, Box-LMB)相结合, 提出交互式箱粒子标签多伯努利滤波器(IMM-Box-LMB)算法.该算法首先通过扩展多目标状态, 引入模型匹配概率变量, 并利用量测信息在预测阶段更新模型匹配概率, 进而使用交互式多模型算法对每个箱粒子状态进行混合估计.其次, 在更新阶段提出二次收缩算法, 通过二次收缩算法使更新后的箱粒子具有更大的区间和存活概率, 也更加接近真实目标位置, 从而达到提升后续时刻箱粒子多样性的目的.仿真结果表明, 二次收缩算法能够有效地提升箱粒子的多样性.将二次收缩算法应用于IMM-Box-LMB算法, 能够在不同信噪比下稳定准确地估计机动目标的个数.相同条件下, 与匀速直线运动(Constant velocity, CV)模型下的Box-LMB算法相比, IMM-Box-LMB算法能够对多机动目标的数目以及状态进行更加有效的估计.Abstract: For the problem that the multiple maneuvering target tracking, an interacting multiple model box labeled multi-bernoulli (IMM-Box-LMB) filter is proposed. Firstly, by introducing the model matching probability variable, and using the measurement information to update it in the predictive stage of the IMM-Box-LMB filter, the interactive multiple model algorithm is used to estimate the state of each box particle. Secondly, put forward the quadratic contraction algorithm in the update stage of the filter. By using the quadratic contraction algorithm in the update stage of IMM-Box-LMB filter, the box particles will have greater interval and greater probability of survival after update, thus achieving the purpose to improve the diversity of the box particle. Simulation shows that the quadratic contraction algorithm can effectively improve the diversity of the box particle. Through using the quadratic contraction algorithm in the IMM-Box-LMB filter, the number of targets can be estimated stably and accurately under different clutter rates.In the same conditions, compared with the CV-Box-LMB algorithm, the IMM-BOX-LMB algorithm can estimate the cardinality and state of targets more effectively.
-
Key words:
- Multiple maneuvering target tracking /
- interacting multiple model /
- labeled multi-Bernoulli particle filter /
- box particle filter /
- contraction algorithm
1) 本文责任编委 刘允刚 -
图 2 传统收缩算法和二次收缩算法下箱粒子状态更新
Fig. 2 The box particle state update by using the traditional contraction algorithm and the quadratic contraction algorithm, respectively
图 4 IMM-Box-LMB算法对目标状态估计与目标航迹跟踪效果
Fig. 4 True trajectories and estimates of targets using the IMM-Box-LMB algorithm
图 5 IMM-Box-LMB算法使用二次收缩算法和传统收缩算法更新之后的有效箱粒子数的比值(50 MC)
Fig. 5 Count the number of effective box particles for IMM-Box-LMB algorithm using the quadratic contraction algorithm and the traditional contraction algorithm, respectively, and then return to their ratio (50 MC)
图 6 IMM-Box-LMB算法使用二次收缩算法和传统收缩算法对多机动目标的势估计(50 MC)
Fig. 6 Cardinality statistics returned by IMM-Box-LMB algorithm using the traditional contraction algorithm and the quadratic contraction algorithm, respectively (50 MC)
图 7 IMM-Box-LMB算法使用二次收缩算法和传统收缩算法之后(a) OSPA距离和OSPA成分(b)位置和(c)势估计$p = 1$, $c = 5$ (50 MC)
Fig. 7 The OSPA of IMM-Box-LMB algorithm using the traditional contraction algorithm and the quadratic contraction algorithm, respectively. (a) OSPA distance and OSPA components (b) localization component (c) cardinality component $p=1$, $c = 5$ (50MC)
图 8 IMM-Box-LMB, CV-Box-LMB不同杂波率下目标个数估计(50 MC)
Fig. 8 IMM-Box-LMB, CV-Box-LMB cardinality estimates under different clutter rates (50 MC)
图 9 不同杂波率下IMM-Box-LMB算法和CV-Box-LMB算法OSPA距离(a)和OSPA成分位置(b)和势(c)估计$p = 1$, $c = 5$ (50 MC)
Fig. 9 Under different clutter rates, (a) The OSPA distance and OSPA components. (b) localization component (c) cardinality component $(p = 1, c = 5)$ using IMM-Box-LMB algorithm and CV-Box-LMB algorithm, respectively (50 MC)
-
[1] Blackman S S. Multiple hypothesis tracking for multiple target tracking. IEEE Aerospace and Electronic Systems Magazine, 2004, 19(1): 5-18 [2] Chang K C, Bar-Shalom Y. Joint probabilistic data association for multitarget tracking with possibly unresolved measurements and maneuvers. IEEE Transactions on Automatic Control, 1984, 29(7): 585-594 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=6a84b10cd4663c41e72d499a60b0cf3d [3] Mahler R P S. Statistical Multisource-Multitarget Information Fusion. London: Artech House, 2007. 565-682 [4] Mahler R P S. Multitarget Bayes filtering via first-order multitarget moments. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152-1178 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=1de4826f6f0ff5b11dbc1832d7b3a490 [5] Mahler R. PHD filters of higher order in target number. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1523-1543 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=e917996c66d0f95baad191a9741e30df [6] Mahler R P S. Statistical Multisource-Multitarget Information Fusion. London: Artech House, 2007. 110-120 [7] Vo B T, Vo B N, Cantoni A. The cardinality balanced multi-target multi-Bernoulli filter and its implementations. IEEE Transactions on Signal Processing, 2009, 57(2): 409-423 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=61dfd7f286ae58058d68670341ea1672 [8] Vo B T, Vo B N. Labeled random finite sets and multi-object conjugate priors. IEEE Transactions on Signal Processing, 2013, 61(13): 3460-3475 [9] Vo B N, Vo B T, Phung D. Labeled random finite sets and the Bayes multi-target tracking filter. IEEE Transactions on Signal Processing, 2014, 62(24): 6554-6567 [10] Reuter S, Vo B T, Vo B N, Dietmayer K. The labeled multi-Bernoulli filter. IEEE Transactions on Signal Processing, 2014, 62(12): 3246-3260 [11] Papi F, Vo B N, Vo B T, Fantacci C, Beard M. Generalized labeled multi-Bernoulli approximation of multi-object densities. IEEE Transactions on Signal Processing, 2015, 63(20): 5487-5497 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=69f387345082d430d7add445f6c3c32c [12] 邱昊, 黄高明, 左炜, 高俊.多模型标签多伯努利机动目标跟踪算法.系统工程与电子技术, 2015, 37(12): 2683-2688 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xtgcydzjs201512003Qiu Hao, Huang Gao-Ming, Zuo Wei, Gao Jun. Multiple model labeled multi-Bernoulli filter for maneuvering target tracking. Systems Engineering and Electronics, 2015, 37(12): 2683-2688 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xtgcydzjs201512003 [13] 袁常顺, 王俊, 向洪, 孙进平.基于VB近似的自适应$\delta$-GLMB滤波算法.系统工程与电子技术, 2017, 39(2): 237-243Yuan Chang-Shun, Wang Jun, Xiang Hong, Sun Jin-Ping. Adaptive $\delta$-GLMB filtering algorithm based on VB approximation. Systems Engineering and Electronics, 2017, 39(2): 237-243 [14] 朱书军, 刘伟峰, 崔海龙.基于广义标签多伯努利滤波的可分辨群目标跟踪算法.自动化学报, 2017, 43(12): 2178-2189 doi: 10.16383/j.aas.2017.c160334Zhu Shu-Jun, Liu Wei-Feng, Cui Hai-Long. Multiple resolvable groups tracking using the GLMB filter. Acta Automatica Sinica, 2017, 43(12): 2178-2189 doi: 10.16383/j.aas.2017.c160334 [15] Reuter S, Scheel A, Dietmayer K. The multiple model labeled multi-Bernoulli filter. In: Proceedings of the 18th International Conference on Information Fusion. Washington, DC, USA: IEEE, 2015. 1574-1580 [16] Abdallah F, Gning A, Bonnifait P. Box particle filtering for nonlinear state estimation using interval analysis. Automatica, 2008, 44(3): 807-815 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=4f54944c69790e29fab9581937904202 [17] Jaulin L, Kieffer M, Didrit O, Walter É. Applied Interval Analysis. London: Springer, 2001. 11-43 [18] Luc Jaulin. Computing minimal-volume credible sets using interval analysis; application to bayesian estimation. IEEE Transactions on Signal Processing, 2006, 54(9): 3632-3636 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0c257c984c945138181d145de20b15b5 [19] 苗雨, 宋骊平, 姬红兵.箱粒子广义标签多伯努利滤波的目标跟踪算法.西安交通大学学报, 2017, 51(10): 107-112 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xajtdxxb201710018Miao Yu, Song Li-Ping, Ji Hong-Bing. Target tracking method with box-particle generalized label multi-Bernoulli filtering. Journal of Xi'an Jiaotong University, 2017, 51(10): 107-112 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xajtdxxb201710018 [20] 魏帅, 冯新喜, 王泉, 鹿传国.基于箱粒子滤波的鲁棒标签多伯努利跟踪算法.兵工学报, 2017, 38(10): 2062-2068 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=bgxb201710024Wei Shuai, Feng Xin-Xi, Wang Quan, Lu Chuan-Guo. Robust labeled multi-Bernoulli tracking algorithm based on box particle filtering. Acta Armamentarii, 2017, 38(10): 2062-2068 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=bgxb201710024 [21] Mazor E, Averbuch A, Bar-Shalom Y, Dayan J. Interacting multiple model methods in target tracking: a survey. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(1): 103-123 [22] Ristic B, Arulampalam S, Gordon N. Beyond the Kalman Filter: Particle Filters for Tracking Applications. London: Artech House, 2004. 24-28 [23] Boers Y, Driessen J N. Interacting multiple model particle filter. IEE Proceedings - Radar, Sonar and Navigation, 2003, 150(5): 344-349 [24] Ristic B, Vo B N, Clark D, Vo B T. A metric for performance evaluation of multi-target tracking algorithms. IEEE Transactions on Signal Processing, 2011, 59(7): 3452-3457 -
下载:
计量
- 文章访问数: 1225
- HTML全文浏览量: 135
- PDF下载量: 137
- 被引次数: 0
