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量测不确定性条件下的箱粒子滤波目标跟踪方法

王宁 段睿 周笑仪

王宁, 段睿, 周笑仪. 量测不确定性条件下的箱粒子滤波目标跟踪方法[J]. 电子与信息学报, 2024, 46(9): 3654-3661. doi: 10.11999/JEIT231439
引用本文: 王宁, 段睿, 周笑仪. 量测不确定性条件下的箱粒子滤波目标跟踪方法[J]. 电子与信息学报, 2024, 46(9): 3654-3661. doi: 10.11999/JEIT231439
WANG Ning, DUAN Rui, ZHOU Xiaoyi. A Target Tracking Method Based on Box-particle Filter Under Measurement Uncertainty[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3654-3661. doi: 10.11999/JEIT231439
Citation: WANG Ning, DUAN Rui, ZHOU Xiaoyi. A Target Tracking Method Based on Box-particle Filter Under Measurement Uncertainty[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3654-3661. doi: 10.11999/JEIT231439

量测不确定性条件下的箱粒子滤波目标跟踪方法

doi: 10.11999/JEIT231439
基金项目: 国家自然科学基金(12074315, 62101549),太仓市基础研究计划 (TC2023JC18)
详细信息
    作者简介:

    王宁:男,博士生,研究方向为水声物理场和水下目标跟踪检测方法

    段睿:男,副研究员,研究方向为水声工程和水下电子信息技术

    周笑仪:女,博士生,研究方向为深海物理特性及深度分集探测

    通讯作者:

    段睿 duanrui@nwpu.edu.cn

  • 中图分类号: TP929.3

A Target Tracking Method Based on Box-particle Filter Under Measurement Uncertainty

Funds: The National Natural Science Foundation of China (12074315, 62101549), The Fundamental Research Project of Taicang (TC2023JC18)
  • 摘要: 在复杂水下环境中,主动声呐的量测值在距离和方位分辨率上存在较大的不确定性,即一个目标回波的能量可能覆盖声呐距离-方位能量谱的多个相邻位置网格。并且,当环境中混响较强时,上述量测不确定性将引起多个区域性的杂波干扰。为了减小状态空间估计的偏差,基于粒子滤波(PF)的跟踪方法需要大量粒子来近似后验概率密度,跟踪的实时性急剧降低。针对上述问题,该文提出一种基于区间量测的箱粒子滤波跟踪方法(IBPF),对主动声呐量测值进行区间表示,即用一个表示距离和方位区间的箱粒子代替点值量测,用区间表示这种量测不确定性,在提高状态估计稳定性的同时,极大程度地减少了后验概率密度估计所需的粒子数,从而进一步提高计算效率。实验结果表明,所提IBPF与PF相比,能以更高的计算效率获得更优的跟踪性能,对目标的跟踪时间缩短了18.06%,跟踪成功帧数增加了4.29%。
  • 图  1  不同粒子数时,PF算法在100次MC实验中的平均性能

    图  2  不同粒子数时,IBPF算法在100次MC实验中的平均性能

    图  3  IBPF算法性能随箱粒子数变化情况

    图  4  海上实测数据集

    图  5  海上实测数据跟踪结果

    图  6  实验数据集中目标跟踪的后验概率对比

    图  7  PF算法和IBPF算法的径向距离差和方位角度差的交叉对比结果

    表  1  仿真实验平均计算时间(s)

    粒子数/箱粒子数 PF IBPF
    1000 13.27
    2000 16.25
    5000 31.06
    10000 43.82
    5 16.32
    10 18.29
    25 22.80
    50 29.87
    下载: 导出CSV

    1  IBPF算法

     参数初始化:$ {p_{k\mid k}} $,$ {p_{\text{b}}} $,$ {p_{\text{s}}} $,$ {{{Z}}_k} $,$ \{ \begin{array}{*{20}{c}} {\omega _k^i,}&{[{\boldsymbol{x}}_k^i]} \end{array}\} _{i = 1}^N $
     1. 通过式(14)进行箱粒子状态更新,得到$ [{\boldsymbol{x}}_{p,k}^i] $;
     2. 基于$ \mathop Z\nolimits_k $新生箱粒子$ \{ {\omega _{{\mathrm{b}},k}^i,}\;\; {[{\boldsymbol{x}}_{{\mathrm{b}},k}^i]} \} _{i = 1}^{{N_{\mathrm{b}}}} $,其中
     $ \omega _{{\mathrm{b}},k}^i = 1/{N_{\mathrm{b}}} $;
     3. 通过式(14)进行新生箱粒子状态更新,得到$ [{\boldsymbol{x}}_{{\mathrm{b}},k + 1}^i] $;
     4. 由式(7)更新目标后验概率$ {p_{k + 1\mid k}} $;
     5. 由式(15)预测$k{\text{ + 1}}$时刻持续存在的箱粒子权重$ \omega _{{\mathrm{p}},k + 1}^i $;
     6. 由式(16)预测$k{\text{ + 1}}$时刻的新生箱粒子权重$ \omega _{{\mathrm{b}},k + 1}^i $;
     7. 进行箱粒子权重联合,得到$ \{ \begin{array}{*{20}{c}} {\omega _{k + 1|k}^i,}&{[{\boldsymbol{x}}_{k + 1|k}^i]} \end{array}\} _{i = 1}^{{N_{\mathrm{u}}}} $;
     8. 通过式(11)计算每个箱粒子与量测$ \mathop {{Z}}\nolimits_{k + 1} $的似然函数值;
     9. 由式(9)和式(10)更新得到$k{\text{ + 1}}$时刻的后验概率$ {p_{k + 1\mid k + 1}} $;
     10. 由式(18)进行箱粒子权重更新,归一化处理后进行重采样,
     得到$ \{ \begin{array}{*{20}{c}} {\omega _{k + 1}^i = 1/N,}&{[{\boldsymbol{x}}_{k + 1}^i]} \end{array}\} _{i = 1}^N $;
     11. 进行循环步骤1–步骤10,到$K$时刻结束;
     12. 结束循环,输出目标后验概率和状态估计。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-01-02
  • 修回日期:  2024-07-10
  • 网络出版日期:  2024-08-02
  • 刊出日期:  2024-09-26

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