A Cardinalized Probability Hypothesis Density Filter with Unknown Clutter Estimation Using Corrected Sample Set
-
摘要: 在贝叶斯框架下的多目标跟踪算法中,总是假设杂波的先验信息是已知的。然而,实际应用中,杂波分布一般是未知的,假设的杂波分布往往与实际情况匹配度差,难以保证滤波精度。针对该问题,该文研究了未知杂波势估计概率假设密度(CPHD)滤波算法。首先,提出一种基于狄利克雷过程混合模型(DPMM)类的未知杂波CPHD算法,该算法能够自动选取合适的类数对杂波进行描述,有效降低了杂波空间分布估计的误差。此外,提出样本集校正的思想,并将其引入所提算法,通过去除样本集中由真实目标产生的量测,较好地解决了杂波数过估和目标数低估的问题。与传统算法相比,所提算法的滤波精度更接近于杂波信息匹配情况下的性能,仿真结果验证了其优越性与鲁棒性。
-
关键词:
- 多目标跟踪 /
- 参数估计 /
- 未知杂波 /
- 狄利克雷过程混合模型 /
- 势估计概率假设密度滤波
Abstract: In multi-target tracking algorithms under the Bayesian filtering framework, it is usually assumed that the priori knowledge of clutter is known. However, in practice, the knowledge of clutter is usually unknown, and the assumption of clutter may not agree with the truth, resulting in the filtering precision declining. For this problem, this paper addresses the problem of Cardinalized Probability Hypothesis Density (CPHD) filter with clutter estimation. Firstly, this paper presents a new CPHD filter with clutter estimation based on Dirichlet Process Mixture Model (DPMM). Thus, this DPMM--CPHD algorithm can reduce the estimation error of the clutter spatial distribution effectively by selecting an appropriate class number. Secondly, to solve the clutter overestimation and cardinality underestimation problems, a correction idea of the sample set via CPHD filter recursion is proposed. By introducing this idea to the DPMM--CPHD algorithm, an improved DPMM--CPHD algorithm is proposed to solve this intractability of errors on clutter number and target number. Simulation results show that the proposed algorithm can effectively estimate the unknown parameters of clutter and has a good performance of multi-target tracking. -
LI Bo and PANG Fuwen. Improved probability hypothesis density filter for multitarget tracking[J]. Nonlinear Dynamics, 2014, 76(1): 367-376. doi: 10.1007/s11071-013-1131-1. SI Weijian, WANG Liwei, and QU Zhiyu. A measurement- driven adaptive probability hypothesis density filter for multitarget tracking[J]. Chinese Journal of Aeronautics, 2015, 28(6): 1689-1698. doi: 10.1016/j.cja.2015.10.004. 刘俊, 刘瑜, 何友, 等. 杂波环境下基于全邻模糊聚类的联合概率数据互联算法[J]. 电子与信息学报, 2016, 38(6): 1438-1445. doi: 10.11999/JEIT150849. LIU Jun, LIU Yu, HE You, et al. Joint probabilistic data association algorithm based on all-neighbor fuzzy clustering in clutter[J]. Journal of Electronics Information Technology, 2016, 38(6): 1438-1445. doi: 10.11999/JEIT150849. 徐从安, 何友, 夏沭涛, 等. 基于随机摄动再采样的粒子概率假设密度滤波器[J]. 电子与信息学报, 2016, 38(11): 2819-2825. doi: 10.11999/JEIT160114. XU Cong'an, HE You, XIA Shutao, et al. Particle probability hypothesis density filter based on stochastic perturbation resampling[J]. Journal of Electronics Information Technology, 2016, 38(11): 2819-2825. doi: 10.11999/JEIT 160114. 袁常顺, 王俊, 孙进平, 等. 一种幅度信息辅助多伯努利滤波算法[J]. 电子与信息学报, 2016, 38(2): 464-471. doi: 10.11999 /JEIT150683. YUAN Changshun, WANG Jun, SUN Jinping, et al. A multi- Bernoulli filtering algorithm using amplitude information[J]. Journal of Electronics Information Technology, 2016, 38(2): 464-471. doi: 10.11999/JEIT150683. YANG Jinlong and GE Hongwei. Adaptive probability hypothesis density filter based on variational Bayesian approximation for multi-target tracking[J]. Radar Sonar Navigation Iet, 2013, 7(9): 959-967. doi: 10.1049/iet-rsn.2012. 0357. 杨峰, 张婉莹. 一种多模型贝努利粒子滤波机动目标跟踪算法[J]. 电子与信息学报, 2017, 39(3): 634-639. doi: 10.11999/ JEIT160467. YANG Feng and ZHANG Wanying. Multiple model Bernoulli particle filter for maneuvering target tracking[J]. Journal of Electronics Information Technology, 2017, 39(3): 634-639. doi: 10.11999/JEIT160467. MAHLER Ronald. Multi-target Bayes filtering via first-order multi-target moments[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 16(2): 11521178. doi: 10.1109 /TAES.2003.1261119. 吴卫华, 江晶, 冯讯, 等. 基于高斯混合势化概率假设密度的脉冲多普勒雷达多目标跟踪算法[J]. 电子与信息学报, 2015, 37(6): 1490-1494. doi: 10.11999/JEIT141232. WU Weihua, JIANG Jing, FENG Xun, et al. Multi-target tracking algorithm based on Gaussian mixture cardinalized probability hypothesis density for pulse doppler radar[J]. Journal of Electronics Information Technology, 2015, 37(6): 1490-1494. doi: 10.11999/JEIT141232. VO Ba Tuong, VO Ba Ngu, and CANTONI Antonio. Analytic implementations of the cardinalized probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3553-3567. doi: 10.1109/TSP.2007. 894241. MAHLER Ronald, VO Ba Tuong, and VO Ba Ngu. 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. BEARD Michael, VO Ba Tuong, and VO Ba Ngu. Multitarget filtering with unknown clutter density using a bootstrap GMCPHD filter[J]. IEEE Signal Processing Letters, 2013, 20(4): 323-326. doi: 10.1109/LSP.2013.2244594. LIAN Feng, HAN Chongzhao, and LIU Weifeng. Estimating unknown clutter intensity for PHD filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(4): 2066-2078. doi: 10.1109/TAES.2010.5595616. LIU Weifeng, CUI Hailong, and WEN Chenglin. A time- varying clutter intensity estimation algorithm by using Gibbs sampler and BIC[C]. IEEE International Conference on Information Fusion, Heidelberg, Germany, 2016: 1-8. NSOESIE Elaine O, LEMAN Scotland C, and MARATHE Marathe V. A Dirichlet process model for classifying and forecasting epidemic curves[J]. Bmc Infectious Diseases, 2014, 14(2): 1-12. doi: 10.1186/1471-2334-14-12. WANG Lu, ZHAO Lifan, BI Guoan, et al. Novel wideband DOA estimation based on sparse Bayesian learning with Dirichlet process priors[J]. IEEE Transactions on Signal Processing, 2016, 64(2): 275-289. doi: 10.1109/TSP.2015. 2481790. MUTHUKUMARANA Saman, and TIWARI Ram C. Meta- analysis using Dirichlet process[J]. Statistical Methods in Medical Research, 2016, 25(1): 352. doi: 10.1177/ 0962280212453891. SUN Xing, YUNG Nelson H C, and LAM Edmund Y. Unsupervised tracking with the doubly stochastic Dirichlet process mixture model[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(9): 2594-2599. doi: 10.1109 /TITS.2016.2518212. BLEI D M, GRIFFITHS T L, and JORDAN M I. The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies[J]. Journal of the ACM, 2010, 57(2): 1-30. doi: 10.1145/1667053.1667056.
点击查看大图
计量
- 文章访问数: 1111
- HTML全文浏览量: 165
- PDF下载量: 153
- 被引次数: 0