运动多平台无源跟踪截尾不敏卡尔曼滤波算法

骆卉子, 曲长文, 冯奇

骆卉子, 曲长文, 冯奇. 运动多平台无源跟踪截尾不敏卡尔曼滤波算法[J]. 信号处理, 2016, 32(12): 1434-1439. DOI: 10.16798/j.issn.1003-0530.2016.12.007
引用本文: 骆卉子, 曲长文, 冯奇. 运动多平台无源跟踪截尾不敏卡尔曼滤波算法[J]. 信号处理, 2016, 32(12): 1434-1439. DOI: 10.16798/j.issn.1003-0530.2016.12.007
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LUO Hui-zi, QU Chang-wen, FENG Qi. Truncated Unscented Kalman Filtering Algorithm for Target Tracking Using Moving Multi-platform[J]. JOURNAL OF SIGNAL PROCESSING, 2016, 32(12): 1434-1439. DOI: 10.16798/j.issn.1003-0530.2016.12.007
Citation: LUO Hui-zi, QU Chang-wen, FENG Qi. Truncated Unscented Kalman Filtering Algorithm for Target Tracking Using Moving Multi-platform[J]. JOURNAL OF SIGNAL PROCESSING, 2016, 32(12): 1434-1439. DOI: 10.16798/j.issn.1003-0530.2016.12.007
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运动多平台无源跟踪截尾不敏卡尔曼滤波算法

  • 海军航空工程学院电子信息工程系

基金项目: 泰山学者建设工程专项经费资助
详细信息
    通讯作者:

    骆卉子   E-mail: jessica_lhz@163.com

  • 中图分类号: TN958.97

Truncated Unscented Kalman Filtering Algorithm for Target Tracking Using Moving Multi-platform

  • Dep. Electronic and Information Engineering, Naval Aeronautical and Astronautical University

More Information
    Corresponding author:

    LUO Hui-zi   E-mail: jessica_lhz@163.com

摘要

    摘要
  • 摘要: 针对已有非线性滤波算法用于运动多平台无源跟踪时精度不高的问题，提出了一种新的跟踪算法即截尾不敏卡尔曼滤波（TUKF）算法以改善跟踪性能。该算法对状态先验概率密度函数及测量噪声概率密度函数进行截尾处理，使其变为具有有界支撑集的函数，并在此基础上结合原始状态先验概率密度函数设计了混合先验概率密度函数，然后针对其中的两种先验概率密度函数，分别应用不敏变换计算对应的后验概率密度函数的前两阶矩信息，并对其进行融合处理得到最终状态估计。仿真结果表明相对于几种典型的非线性滤波算法，TUKF算法能有效改善跟踪性能。
    Abstract: In order to solving the low precision problem of exsiting nonlinear filtering algorithms when used for moving multi-platform passive tracking, a truncated unscented Kalman filtering(ITUKF) is poposed. The proposed algorithm truncates the probability density function (PDF) of the measurement noise and the prior PDF of the state to make them have a bounded support. Combing with the original prior PDF of the state, a mixture prior PDF of the state is designed. The unscented transformation (UT) is applied to each of the prior PDF to calculate first two moments of the corresponding posterior PDF and then these moments are merged to form the final state estimation. Simulation results indicate that compared with several typical nonlinear filtering algorithms, the TUKF algorithm can effectively improves the tracking performance.
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出版历程
  • 收稿日期:  2016-05-10
  • 修回日期:  2016-08-24
  • 发布日期:  2016-12-24

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