Student’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli Filter
-
摘要: 在有重尾的过程噪声和量测噪声的影响下，高斯混合势均衡多目标多伯努利滤波器(GM-CBMeMBer)的滤波性能会明显下降。针对上述问题，该文提出一种新的学生 t 混合势均衡多目标多伯努利滤波器(STM-CBMeMBer)。该滤波器将过程噪声和量测噪声近似为学生 t 分布，并用学生 t 混合模型来近似多目标的先验强度。从理论上推导出学生 t 混合形式的预测强度和后验强度，建立了势均衡多目标多伯努利滤波器的闭式递推框架。仿真结果表明，在重尾的过程噪声和量测噪声存在的环境中，该滤波器能有效抑制其干扰，相比于传统方法，具有更高的跟踪精度。
-
关键词:
- 多目标跟踪 /
- 重尾噪声 /
- 势均衡多目标多伯努利 /
- 学生 t 分布 /
- 闭式递推框架
Abstract: The filtering performance of Gaussian Mixture Cardinality Balanced Multi-target Multi-Bernoulli (GM-CBMeMBer) filter can be effected by the heavy-tailed process noise and measurement noise. To solve this problem, a new STudent’s t Mixture Cardinality Balanced Multi-target Multi-Bernoulli (STM-CBMeMBer) filter is proposed. The process noise and measurement noise approximately obey the Student’s t distribution in the filter, where the Student’s t mixture model is used to describe approximately the posterior intensity of the multi-target. The predictive intensity and posterior intensity of Student’s t mixture form are deduced theoretically, and the closed recursive framework of cardinality balanced multi-target multi-Bernoulli filter is established. The simulation results show that, in the presence of the heavy-tailed process noise and the measurement noise, the filter can effectively suppress its interference, its tracking accuracy is superior over the traditional methods. -
LEUNG K Y K, INOSTROZA F, and ADAMS M. Relating random vector and random finite set estimation in navigation, mapping, and tracking[J]. IEEE Transactions on Signal Processing, 2017, 65(17): 4609–4623. doi: 10.1109/TSP.2017.2701330 JIN Xuebo, SUN Shuli, WEI Hong, et al. Advances in multi-sensor information fusion: Theory and applications 2017[J]. Sensors, 2018, 18(4): 1162. doi: 10.3390/s18041162 MAHLER R. PHD filters of higher order in target number[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1523–1543. doi: 10.1109/TAES.2007.4441756 MAHLER R P S, VO B T, and VO B N. CPHD filtering with unknown clutter rate and detection profile[J]. IEEE Transactions on Signal Processing, 2011, 59(8): 3497–3513. doi: 10.1109/TSP.2011.2128316 MAHLER R P S. Statistical Multisource-Multitarget Information Fusion[M]. Boston: Artech House, Inc., 2007: 655-682. VO B T, VO B N, and CANTONI A. The cardinality balanced multi-target multi-Bernoulli filter and its implementations[J]. IEEE Transactions on Signal Processing, 2009, 57(2): 409–423. doi: 10.1109/TSP.2008.2007924 YANG Jinlong and GE Hongwei. Adaptive probability hypothesis density filter based on variational bayesian approximation for multi-target tracking[J]. IET Radar, Sonar, & Navigation, 2013, 7(9): 959–967. doi: 10.1049/iet-rsn.2012.0357 HUANG Yulong, ZHANG Yonggang, LI Ning, et al. Robust student’s t based nonlinear filter and smoother[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(5): 2586–2596. doi: 10.1109/TAES.2016.150722 TRONARP F, HOSTETTLER R, and SÄRKKÄ S. Sigma-point filtering for nonlinear systems with non-additive heavy-tailed noise[C]. The 19th International Conference on Information Fusion, Heidelberg, Germany, 2016: 1859–1866. HUANG Yulong, ZHANG Yonggang, LI Ning, et al. A robust student’s t based cubature filter[C]. The 19th International Conference on Information Fusion, Heidelberg, Germany, 2016: 9–16. ROTH M, ÖZKAN E, and GUSTAFSSON F. A student’s t filter for heavy tailed process and measurement noise[C]. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, Canada, 2013: 5770–5774. SCHUHMACHER D, VO B T, and VO B N. A consistent metric for performance evaluation of multi-object filters[J]. IEEE Transactions on Signal Processing, 2008, 56(8): 3447–3457. doi: 10.1109/tsp.2008.920469 TIAN Xin, YUAN Ting, and BAR-SHALOM Y. Track-to-track fusion in linear and nonlinear systems[C]. Selected Papers of the Itzhack Y. Bar-Itzhack Memorial Symposium on Estimation, Navigation, and Spacecraft Control, Berlin Heidelberg, 2015: 21–41. doi: 10.1007/978-3-662-44785-7_2. MCNEIL A J. Multivariate t distributions and their applications[J]. Journal of the American Statistical Association, 2006, 101(473): 390–391. doi: 10.1198/jasa.2006.s67 -
下载:
图(4)
计量
- 文章访问数: 3142
- HTML全文浏览量: 1023
- PDF下载量: 74
- 被引次数: 0
下载:
