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基于自适应的增广状态-交互式多模型的机动目标跟踪算法

许红 谢文冲 袁华东 段克清 王永良

许红, 谢文冲, 袁华东, 段克清, 王永良. 基于自适应的增广状态-交互式多模型的机动目标跟踪算法[J]. 电子与信息学报, 2020, 42(11): 2749-2755. doi: 10.11999/JEIT190516
引用本文: 许红, 谢文冲, 袁华东, 段克清, 王永良. 基于自适应的增广状态-交互式多模型的机动目标跟踪算法[J]. 电子与信息学报, 2020, 42(11): 2749-2755. doi: 10.11999/JEIT190516
Hong XU, Wenchong XIE, Huadong YUAN, Keqing DUAN, Yongliang WANG. Maneuvering Target Tracking Algorithm Based on the Adaptive Augmented State Interracting Multiple Model[J]. Journal of Electronics and Information Technology, 2020, 42(11): 2749-2755. doi: 10.11999/JEIT190516
Citation: Hong XU, Wenchong XIE, Huadong YUAN, Keqing DUAN, Yongliang WANG. Maneuvering Target Tracking Algorithm Based on the Adaptive Augmented State Interracting Multiple Model[J]. Journal of Electronics and Information Technology, 2020, 42(11): 2749-2755. doi: 10.11999/JEIT190516

基于自适应的增广状态-交互式多模型的机动目标跟踪算法

doi: 10.11999/JEIT190516
基金项目: 国家自然科学基金(61871397)
详细信息
    作者简介:

    许红:男,1991年生,博士生,研究方向为雷达数据处理

    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

    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

    段克清:男,1981年生,副教授,主要研究方向为空时自适应信号处理、阵列信号处理等

    王永良:男,1965年生,教授,主要研究方向为雷达信号处理、空时自适应信号处理等

    通讯作者:

    许红 xuhongzhxu@163.com

  • 中图分类号: TN957.51; TP391.41

Maneuvering Target Tracking Algorithm Based on the Adaptive Augmented State Interracting Multiple Model

Funds: The National Natural Science Foundation of China (61871397)
  • 摘要: 现有的增广状态-交互式多模型算法存在着依赖于量测噪声协方差矩阵这一先验信息的问题。当先验信息未知或不准确时,算法的跟踪性能将会下降。针对上述问题,该文提出一种自适应的变分贝叶斯增广状态-交互式多模型算法VB-AS-IMM。首先,针对增广状态的跳变马尔科夫系统,该文给出了联合估计增广状态和量测噪声协方差矩阵的变分贝叶斯推断概率模型。其次,通过理论推导证明了该概率模型是非共轭的。最后,通过引入一种“信息反馈+后处理”方案,提出联合后验密度的次优求解方法。所提算法能够在线估计未知的量测噪声协方差矩阵,具有更强的鲁棒性和适应性。仿真结果验证了算法的有效性。
  • 图  1  仿真场景

    图  2  量测协方差矩阵先验信息不准确时算法跟踪性能

    图  3  各算法的模型概率

    表  1  算法的平均RMSE

    算法位置RMSE(m)速度RMSE(m/s)
    MIMM131.692.37
    IMM191.913.37
    MAS-IMM78.900.95
    AS-IMM90.941.47
    VB-AS-IMM79.740.96
    下载: 导出CSV
  • BLOM H A P and BAR-SHALOM Y. The interacting multiple model algorithm for systems with Markovian switching coefficients[J]. IEEE Transactions on Automatic Control, 1988, 33(8): 780–783. doi: 10.1109/9.1299
    CHANG C B and ATHANS M. State estimation for discrete systems with switching parameters[J]. IEEE Transactions on Aerospace and Electronic Systems, 1978, AES–14(3): 418–425. doi: 10.1109/TAES.1978.308603
    ANDERSON B D O and MOORE J B. Optimal Filtering[M]. Englewood Cliffs: Prentice-Hall, 1979: 165–190.
    RAUCH H. Solutions to the linear smoothing problem[J]. IEEE Transactions on Automatic Control, 1963, 8(4): 371–372. doi: 10.1109/TAC.1963.1105600
    KELLY C N and ANDERSON B D O. On the stability of fixed-lag smoothing algorithms[J]. Journal of the Franklin Institute, 1971, 291(4): 271–281. doi: 10.1016/0016-0032(71)90183-9
    MOORE J B. Discrete-time fixed-lag smoothing algorithms[J]. Automatica, 1973, 9(2): 163–174. doi: 10.1016/0005-1098(73)90071-X
    MATHEWS V J and TUGNAIT J K. Detection and estimation with fixed lag for abruptly changing systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 1983, AES–19(5): 730–739. doi: 10.1109/TAES.1983.309374
    CHEN Bing and TUGNAIT J K. Interacting multiple model fixed-lag smoothing algorithm for Markovian switching systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(1): 243–250. doi: 10.1109/7.826326
    MORELANDE M R and RISTIC B. Smoothed state estimation for nonlinear Markovian switching systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(4): 1309–1325. doi: 10.1109/TAES.2008.4667711
    LOPEZ R and DANÈS P. Low-complexity IMM smoothing for jump Markov nonlinear systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(3): 1261–1272. doi: 10.1109/TAES.2017.2669698
    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
    TZIKAS D G, LIKAS A C, 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
    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
    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): 5024–5036. doi: 10.1109/TSP.2012.2208106
    MA Yanjun, ZHAO Shunyi, and HUANG Biao. Multiple-model state estimation based on variational Bayesian inference[J]. IEEE Transactions on Automatic Control, 2019, 64(4): 1679–1685. doi: 10.1109/TAC.2018.2854897
    DONG Peng, JING Zhongliang, and LEUNG H. 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
    XU Hong, XIE Wenchong, YUAN Huadong, et al. Fixed-point iteration Gaussian sum filtering estimator with unknown time-varying non-Gaussian measurement noise[J]. Signal Processing, 2018, 153: 132–142. doi: 10.1016/j.sigpro.2018.07.017
    LI Xiaorong and JILKOV V P. Survey of maneuvering target tracking. Part I. Dynamic models[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1333–1364. doi: 10.1109/TAES.2003.1261132
    ZHOU Gongjian, GUO Zhengkun, CHEN Xi, et al. Statically fused converted measurement Kalman filters for phased-array radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(2): 554–568. doi: 10.1109/TAES.2017.2760798
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出版历程
  • 收稿日期:  2019-07-10
  • 修回日期:  2020-02-28
  • 网络出版日期:  2020-09-01
  • 刊出日期:  2020-11-16

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