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基于联合似然函数的多扩展目标广义标签多伯努利滤波器

刘艺多 姬红兵 张永权

刘艺多, 姬红兵, 张永权. 基于联合似然函数的多扩展目标广义标签多伯努利滤波器[J]. 电子与信息学报, 2023, 45(4): 1303-1312. doi: 10.11999/JEIT220213
引用本文: 刘艺多, 姬红兵, 张永权. 基于联合似然函数的多扩展目标广义标签多伯努利滤波器[J]. 电子与信息学报, 2023, 45(4): 1303-1312. doi: 10.11999/JEIT220213
LIU Yiduo, JI Hongbing, ZHANG Yongquan. A Multiple Extended Target Generalized Labeled Multi-Bernoulli Filter Based on Joint Likelihood Function[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1303-1312. doi: 10.11999/JEIT220213
Citation: LIU Yiduo, JI Hongbing, ZHANG Yongquan. A Multiple Extended Target Generalized Labeled Multi-Bernoulli Filter Based on Joint Likelihood Function[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1303-1312. doi: 10.11999/JEIT220213

基于联合似然函数的多扩展目标广义标签多伯努利滤波器

doi: 10.11999/JEIT220213
基金项目: 国家自然科学基金(61871301),中国博士后科学基金(2020T130494, 2018M633470),中央高校基本科研业务费专项资金(XJS210211)
详细信息
    作者简介:

    刘艺多:女,博士生,研究方向为扩展目标跟踪、信号处理

    姬红兵:男,教授,研究方向为目标跟踪、模式识别

    张永权:男,副教授,研究方向为目标跟踪、信息融合

    通讯作者:

    姬红兵 hbji@xidian.edu.cn

  • 中图分类号: TN953

A Multiple Extended Target Generalized Labeled Multi-Bernoulli Filter Based on Joint Likelihood Function

Funds: The National Natural Science Foundation of China (61871301), China Postdoctoral Science Foundation (2020T130494, 2018M633470), The Fundamental Research Funds for the Central Universities (XJS210211)
  • 摘要: 高分辨率雷达监视系统可观测到区域内不同形状的多个扩展目标,可靠的形状估计有利于提高扩展目标跟踪性能,并可作为战场态势评估的重要依据。该文针对不同形状多扩展目标跟踪问题,提出一种基于联合似然函数的广义标签多伯努利(JL-GLMB)滤波器,可实现目标数目、航迹以及形状的精确估计。首先,将目标形状建模为星凸集,并利用非线性量测变换滤波器更新GLMB分布中的高斯分量,有效提高扩展目标状态估计精度。然后,通过对数加权融合策略,构造联合似然函数,综合衡量扩展目标和量测单元之间的相似程度。最后,基于吉布斯采样,提出快速计算扩展目标状态后验概率密度的方法,有效提高数据关联的准确率和计算效率。仿真实验结果表明,所提滤波器能够有效估计不同形状的多扩展目标状态,且在杂波环境下具有稳定的势估计。
  • 图  1  在两种情况下,运动状态和扩展状态似然值示意图

    图  2  两个近距离扩展目标运动轨迹图

    图  3  基于不同似然函数的JL-GLMB滤波器跟踪性能对比

    图  4  多弹头弹道导弹运动轨迹图

    图  5  GPR-GLMB和JL-GLMB滤波器跟踪性能对比

    图  6  GPR-GLMB和JL-GLMB滤波器目标数目估计曲线

    算法1 吉布斯采样算法
     输入:$ {\gamma ^{(1)}},{T_{\text{G}}},\eta ({\gamma _i}) $
     输出:$ {\gamma ^{(1)}},{\gamma ^{(2)}}, \cdots ,{\gamma ^{({T_{\text{G}}})}} $
     初始化:$ c = [ - 1,0, \cdots ,|{\mathcal{U}}({{\mathbf{Z}}_k})|] $;
     $ \hat \eta = [\eta ( - 1),\eta (0), \cdots ,\eta (|{\mathcal{U}}({{\mathbf{Z}}_k})|)] $;
     for $ t = 2,3, \cdots ,{T_{\text{G}}} $ do
      $ {\gamma ^{(t)}} = [{\text{ }}] $;
      for $m = 1,2, \cdots ,|{{\boldsymbol{\varXi}} _{k - 1} }| + |{ {\mathbf{B} }_k}|$ do
        for $ j = 1,2, \cdots ,|{\mathcal{U}}({{\mathbf{Z}}_k})| $ do
        $ {\hat \eta _m}(j) = {\eta _m}(j) $$(1 - {1_{\{ \gamma _{1:m - 1}^{(t)},\gamma _{m + 1:|{{\boldsymbol{\varXi}} _{k - 1} }| + |{ {\mathbf{B} }_k}|}^{(t - 1)}\} } }(j))$;
       end for
       $ \gamma _m^{(t)} \sim {\text{Categorical}}(c,{\hat \eta _m}) $;
      end for
      ${\gamma ^{(t)} } = [\gamma _1^{(t)},\gamma _2^{(t)}, \cdots ,\gamma _{|{\varXi _{k - 1} }| + |{ {\mathbf{B} }_k}|}^{(t)}]$;
     end for
    下载: 导出CSV

    表  1  不同杂波数目下,GPR-GLMB和JL-GLMB滤波器跟踪性能对比(GPR-GLMB/JL-GLMB)

    杂波数目
    050100150200
    OSPA距离14.096 8/4.335 716.660 1/4.459 519.272 8/4.594 521.759 7/4.701 524.350 1/4.830 6
    位置误差6.965 0/0.012 87.568 7/0.013 28.179 7/0.013 38.756 7/0.014 09.340 3/0.014 6
    形状误差10.807 8/8.154 211.091 9/8.181 511.255 9/8.210 811.435 0/8.278 211.644 1/8.297 5
    势误差1.290 0/0.186 03.365 8/0.327 55.421 5/0.469 07.493 6/0.604 29.553 0/0.842 0
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-01
  • 修回日期:  2022-07-08
  • 网络出版日期:  2022-07-15
  • 刊出日期:  2023-04-10

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