角闪烁下基于变分贝叶斯-交互式多模型的目标跟踪

许红; 袁华东; 谢文冲; 刘维建; 王永良

doi:10.11999/JEIT171025

角闪烁下基于变分贝叶斯-交互式多模型的目标跟踪

doi: 10.11999/JEIT171025

^①(海军工程大学武汉 430033) ^②(空军预警学院武汉 430019)

基金项目:

国家自然科学基金(61501505, 61501506)

详细信息

作者简介:
许红：许红：男，1991年生，博士生，研究方向为雷达数据处理、信息融合. 袁华东：男，1985年生，博士生，研究方向为雷达数据处理、阵列信号处理. 谢文冲：男，1978年生，副教授，主要研究方向为机载雷达信号处理、空时自适应信号处理等. 刘维建：男，1982年生，讲师，主要研究方向为空时自适应检测、阵列信号处理. 王永良：男，1965年生，中国科学院院士，主要研究方向为雷达信号处理、空时自适应信号处理等.

中图分类号: TN953
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出版历程
- 收稿日期: 2017-11-02
- 修回日期: 2018-04-03
- 刊出日期: 2018-07-19

Variational Bayesian-interacting Multiple Model Tracking Filter with Angle Glint Noise

XU Hong^① YUAN Huadong^② XIE Wenchong^② LIU Weijian^② WANG Yongliang^②

Funds:

The National Natural Science Foundation of China (61501505, 61501506)

摘要

摘要: 开展角闪烁噪声下的目标跟踪研究对提升传感器的探测性能具有重要意义，其中角闪烁噪声具有的分布未知和非平稳特性是长期困扰研究者的难点。针对该问题，该文首先给出角闪烁下基于变分贝叶斯参数学习的跟踪滤波理论框架。其次，提出一种联合估计运动状态和闪烁噪声分布的变分贝叶斯-交互式多模型(VB-IMM)算法，该算法通过设计多个并行的跟踪模型处理角闪烁的跟踪问题，同时利用变分贝叶斯方法实现闪烁噪声分布参数的在线学习，并反馈给跟踪模型，实时调整跟踪模型参数。最后，设计了仿真实验对算法在闪烁噪声分布未知和非平稳条件下的跟踪性能进行了验证，同时对算法的计算复杂度进行了仿真分析。仿真结果表明，在量测噪声分布未知和非平稳条件下，VB-IMM具有较高的跟踪精度，且算法复杂度较小，易于实现。
- 目标跟踪 /
- 角闪烁噪声 /
- 非平稳 /
- 变分贝叶斯 /
- 交互式多模型
Abstract: Research on target tracking with glint noise is important to improve detection performance of sensor, in which the glint noise’s unknown distribution and non-stationary property puzzle researchers for a long time. In order to solve this problem, the tracking theoretical framework of variational Bayesian parameter learning with glint noise is firstly introduced. Then, a novel algorithm called Variational Bayesian-Interacting Multiple Model (VB-IMM) is proposed to estimate the system states as well as the unknown glint noise’s distribution. The proposed algorithm designs a bank of tracking filters in parallel with different measurement noise. Moreover, the algorithm utilizes variational Bayesian method to learn distribution parameters of the glint noise online and feed these parameters back to the tracking filters to revise the filters. In order to validate the performance of this algorithm, comparative experiments are carried out from two aspects of tracking accuracy and computational complexity. Simulation results verify good performance of tracking error and low computational complexity of the proposed algorithm.
- Target tracking、Angle glint noise、Non-stationary、Variational Bayesian、Interacting Multiple Model (IMM) /

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参考文献(27)

[2] HEWER G A, MARTIN R D, and ZEH J. Robust preprocessing for Kalman filtering of glint noise[J]. IEEE Tranactions on Aerospace and Electronic Systems, 1987, 23(1): 120-128. doi: 10.1109/TAES.1987.313340.

SKOLNIK M. Radar Handbook(Third Edition)[M]. U.S., McGraw Hill Press, 2008: 377-368.

[3] WU Wengrong. Target racking with glint noise[J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(1): 174-185. doi: 10.1109/7.249123.

[4] DAEIPOUR E and BAR-SHALOM Y. IMM tracking of maneuvering targets in the presence of glint[J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(3): 996-1003. doi: 10.1109/7.705913.

LU Cheng, LI Wei, LI Xiangping, et al. Research on target tracking in angular glint noise condition based on improved EKPF algorithm[J]. Tactical Missile Technology, 2017, (2): 81-85. doi: 10.16358/j.issn.1009-1300.2017.02.14.

ZHANG Xueying, CAI Zongping, and WEI Hao. Target tracking based on cubature particle filter algorithm in glint noise environment[J]. Science Technology and Engineering, 2016, (29): 271-274. doi: 10.3969/j.issn.1671-1815.2016.29. 047.

[7] LI Hongwei and WANG Jun. Particle filter for manoeuvring target tracking via passive radar measurements with glint noise[J]. IET Radar, Sonar and Navigation, 2012, 6(3): 180-189. doi: 10.1049/iet-rsn.2011.0075.

[8] KIM J, TANDALE M, MENON P K, et al. Particle filter for ballistic target tracking with glint noise[J]. Journal of Guidance Control and Dynamics, 2010, 33(6): 1918-1921. doi: 10.2514/1.51000.

[9] BILIK I and TABRIKIAN J. Maneuvering target tracking in the presence of glint using the nonlinear Gaussian mixture kalman filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(1): 246-262. doi: 10.1109/TAES. 2010.5417160.

[10] WANG Hongjian and LI Cun. An improved Gaussian mixture CKF algorithm under non-Gaussian observation noise[J]. Discrete Dynamics in Nature and Society, 2016(12): 1-10. doi: 10.1155/2016/1082837.

WANG Lei, CHENG Xianghong, and LI Shuangxi. Gaussian sum high order Unscented Kalman filtering algorithm[J]. Acta Electronica Sinica, 2017, 45(2): 424-430. 10.3969/j.issn. 0372-2112.2017.02.022.

HUANG Binke, WANG Gang, and WANG Wenbing. General proof of observation distance independence of the far-zone angular glint of radar targets[J]. Systems Engineering and Electronics, 2007, 29(4): 505-508. doi: 10.3321/j.issn:1001- 506X.2007.04.002.

[14] BISHOP C M. Pattern Recognition and Machine Learning [M]. New York, Springer, 2006: 423-517.

[15] TZIKAS D G, LIKAS C L, and GALATSANOS N P. The variational approximation for bayesian inference[J]. IEEE Signal Processing Magazine, 2008, 25(6): 131-146. doi: 10.1109/MSP.2008.929620.

[16] BLEI D M, KUCUKELBIR A, and MCAULIFFE J D. Variational inference: A review for statisticians[J]. Journal of The American Statistical Association, 2017, 112(518): 859-877. doi: 10.1080/01621459.2017.1285773.

[17] ZHU Hao, LEUNG H, and HE Zhongshi. State estimation in unknown non-Gaussian measurement noise using variational bayesian technique[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(49): 2601-2614. doi: 10.1109/ TAES.2013.6621839.

[18] SARKKA S and NUMMENMAA A. Recursive noise adaptive kalman filtering by variational bayesian approximations[J]. IEEE Transactions on Automatic Control, 2009, 54(3): 596-600. doi: 10.1109/TAC.2008.2008348.

SHEN Chen, Xu Dingjie, SHEN Feng, et al. Generalized noises adaptive kalman filtering based on variational inference[J]. Systems Engineering and Electronics, 2014, 36(8): 1466-1472. doi: 10.3969/j.issn.1001-506X.2014.08.03.

[20] HUANG Yulong, ZHANG Yonggang, WU Zhemin, et al. A novel adaptive Kalman filter with inaccurate process and measurement noise covariance matrices[J]. IEEE Transactions on Automatic Control, 2018, 63(2): 594-601. doi: 10.1109/TAC.2017.2730480.

[21] ARDESHIRI T, ÖZKAN E, ORGUNER U, et al. Approximate bayesian smoothing with unknown process and measurement noise covariances[J]. IEEE Signal Processing Letters, 2015, 22(12): 2450-2454. doi: 10.1109/LSP.2015. 2490543.

[22] MA Tianli, WANG Xinmin, XIE Rong, et al. Variational bayesian cubature kalman filter for bearing-only tracking in glint noise environment[C]. IEEE Chinese Guidance, Navigation and Control Conference, Nanjing, China, 2016: 232-237.

[23] MIAO Zhiyong, LÜ Yunlong, XU Dingjie, et al. Analysis of a variational Bayesian adaptive cubature Kalman filter tracking loop for high dynamic conditions[J]. Gps Solutions, 2017, 21(1): 111-122. doi: 10.1007/s10291-015-0510-0.

[24] DONG Peng, JING Zhongliang, LEUNG H, et al. Variational bayesian adaptive Cubature information filter based on Wishart distribution[J]. IEEE Transactions on Automatic Control, 2017, 62(11): 6051-6057. doi: 10.1109/TAC.2017. 2704442.

[25] BORDEN B H and MUMFORD M L. A statistical glint/radar cross section target model[J]. IEEE Transactions on Aerospace and Electronic Systems, 1983, 19(5): 781-785. doi: 10.1109/TAES.1983.309383.

[26] ARASARATNAM I and HAYKIN S. Cubature Kalman filters[J]. IEEE Transactions on Automatic Control, 2009, 54(6): 1254-1269. doi: 10.1109/TAC.2009.2019800.

[27] AGAMENNONI G, NIETO J I, and NEBOT E M. Approximate inference in state-space models with heavy- tailed noise[J]. IEEE Transactions on Signal Processing, 2012, 60(10): 5024-5037. doi: 10.1109/TSP.2012.2208106.

[28] LI Tiancheng, BOLIC M, and DJURIC P. Resampling methods for particle filtering: Classification implementation and strategies[J]. IEEE Signal Processing Magazine, 2015, 32(3): 70-86. doi: 10.1109/MSP.2014.2330626.

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