一种基于信息熵的雷达动态自适应选择跟踪方法

葛建军 李春霞

葛建军, 李春霞. 一种基于信息熵的雷达动态自适应选择跟踪方法[J]. 雷达学报, 2017, 6(6): 587-593. doi: 10.12000/JR17081
引用本文: 葛建军, 李春霞. 一种基于信息熵的雷达动态自适应选择跟踪方法[J]. 雷达学报, 2017, 6(6): 587-593. doi: 10.12000/JR17081
Ge Jianjun, Li Chunxia. A Dynamic and Adaptive Selection Radar Tracking Method Based on Information Entropy[J]. Journal of Radars, 2017, 6(6): 587-593. doi: 10.12000/JR17081
Citation: Ge Jianjun, Li Chunxia. A Dynamic and Adaptive Selection Radar Tracking Method Based on Information Entropy[J]. Journal of Radars, 2017, 6(6): 587-593. doi: 10.12000/JR17081

一种基于信息熵的雷达动态自适应选择跟踪方法

DOI: 10.12000/JR17081
基金项目: 装发预研重点基金项目(6140-4130-1021-6DZ9-1001)
详细信息
    作者简介:

    葛建军(1967–),男,研究员,研究方向为雷达系统

    李春霞(1987–),女,北京理工大学博士,高级工程师,研究方向为网络化雷达、信息融合、信息论

    通讯作者:

    李春霞   yunhai_cetc@163.com

A Dynamic and Adaptive Selection Radar Tracking Method Based on Information Entropy

Funds: Key Projects of Equipment Forecast Fund of the General Armament Department (6140-4130-1021-6DZ9-1001)
  • 摘要:

    在现代战争中,战场环境复杂多变,为适应战场探测资源动态组织,基于信息熵,该文提出了定量度量多雷达联合观测获得目标信息量多少的方法,并给出了该信息量的下界。进而,基于最小化每个时刻多雷达观测获得的目标信息熵下界,提出了一种自适应选取信息含量高的雷达进行目标跟踪的方法。仿真结果表明,相比不采用信息熵的跟踪方法,提出的方法具有更高的跟踪精度。

     

  • 图  1  4部雷达位置与目标航迹图

    Figure  1.  Radars locations and target trajectory

    图  2  不同时刻选择的信息量高的雷达编号

    Figure  2.  Radars indexes selected by fusion entropy model

    图  3  目标位置跟踪精度对比

    Figure  3.  Comparison of target position RMSE

    图  4  目标速度跟踪精度对比

    Figure  4.  Comparison of target velocity RMSE

  • [1] Bar-Shalom Y and Li X R. Multitarget-Multisensor Tracking: Principles and Techniques[M]. Storrs, CT: YBS Publishing, 1995.
    [2] Koch W. Tracking and Sensor Data Fusion[M]. Berlin, Heidelberg: Springer, 2014.
    [3] Kalandros M. Covariance control for multisensor systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(4): 1138–1157. DOI: 10.1109/TAES.2002.1145739
    [4] Yang C, Kaplan L, and Blasch E. Performance measures of covariance and information matrices in resource management for target state estimation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(3): 2594–2613. DOI: 10.1109/TAES.2012.6237611
    [5] Jenkins K L and Castañón D A. Information-based adaptive sensor management for sensor networks[C]. Proceedings of 2011 IEEE American Control Conference, San Francisco, CA, 2011: 4934–4940.
    [6] Kay S M. Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory[M]. Englewood Cliffs, N. J.: PTR Prentice hall, 1993.
    [7] Hall D L and McMullen S A H. Mathematical Techniques in Multisensor Data Fusion[M]. Second Edition, Norwood, MA: Artech House, 2004.
    [8] 李春霞. 宽带雷达空间目标及目标群跟踪方法研究[D]. [博士论文], 北京理工大学, 2015.

    Li Chun-xia. Research on space target and targets group tracking methods of wideband radar[D]. [Ph.D. dissertation], Beijing Institute of Technology, 2015.
    [9] Zhang Z T and Zhang J S. A novel strong tracking finite-difference extended Kalman filter for nonlinear eye tracking[J]. Science in China Series F:Information Sciences, 2009, 52(4): 688–694. DOI: 10.1007/s11432-009-0081-1
    [10] 武勇, 王俊. 混合卡尔曼滤波在外辐射源雷达目标跟踪中的应用[J]. 雷达学报, 2014, 3(6): 652–659. DOI: 10.12000/JR14113

    Wu Yong and Wang Jun. Application of mixed Kalman filter to passive radar target tracking[J]. Journal of Radars, 2014, 3(6): 652–659. DOI: 10.12000/JR14113
    [11] Rao B, Xiao S P, and Wang X S. Joint tracking and discrimination of exoatmospheric active decoys using nine-dimensional parameter-augmented EKF[J]. Signal Processing, 2011, 91(10): 2247–2258. DOI: 10.1016/j.sigpro.2011.04.005
    [12] Liu C Y, Shui P L, and Li S. Unscented extended Kalman filter for target tracking[J]. Journal of Systems Engineering and Electronics, 2011, 22(2): 188–192. DOI: 10.3969/j.issn.1004-4132.2011.02.002
    [13] Han Y B. A rao-blackwellized particle filter for adaptive beamforming with strong interference[J]. IEEE Transactions on Signal Processing, 2012, 60(6): 2952–2961. DOI: 10.1109/TSP.2012.2189764
    [14] 李洋漾, 李雯, 易伟, 等. 基于DP-TBD的分布式异步迭代滤波融合算法研究[J]. 雷达学报, 2018, 待出版. DOI: 10.12000/JR17057.

    Li Yangyang, Li Wen, Yi Wei, et al.. A distributedasynchronous recursive filtering fusion algorithm via DPTBD[J]. Journal of Radars, 2018, accepted. DOI: 10.12000/JR17057.
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出版历程
  • 收稿日期:  2017-09-08
  • 修回日期:  2017-11-16
  • 网络出版日期:  2017-12-28

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