Volume 6 Issue 6
Dec.  2017
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2017  >  6(6): 587-593
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
shu

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

DOI: 10.12000/JR17081

  • Information Science Academy, China Electronics Technology Group Corporation, Beijing 100081, China

Funds:  Key Projects of Equipment Forecast Fund of the General Armament Department (6140-4130-1021-6DZ9-1001)
  • Received Date: 2017-09-08
  • Rev Recd Date: 2017-11-16
  • Publish Date: 2017-12-28
    Abstract

  • Nowadays, the battlefield environment has become much more complex and variable. This paper presents a quantitative method and lower bound for the amount of target information acquired from multiple radar observations to adaptively and dynamically organize the detection of battlefield resources based on the principle of information entropy. Furthermore, for minimizing the given information entropy’s lower bound for target measurement at every moment, a method to dynamically and adaptively select radars with a high amount of information for target tracking is proposed. The simulation results indicate that the proposed method has higher tracking accuracy than that of tracking without adaptive radar selection based on entropy.

     

    • FullText(HTML)
  • loading
    • References(14)
  • [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.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(2149) PDF downloads(511) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return