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基于代价参考粒子滤波器组的多目标检测前跟踪算法

卢锦 马令坤 吕春玲 章为川 SUN Chang-Ming

卢锦, 马令坤, 吕春玲, 章为川, SUN Chang-Ming. 基于代价参考粒子滤波器组的多目标检测前跟踪算法. 自动化学报, 2024, 50(4): 1−11 doi: 10.16383/j.aas.c220635
引用本文: 卢锦, 马令坤, 吕春玲, 章为川, SUN Chang-Ming. 基于代价参考粒子滤波器组的多目标检测前跟踪算法. 自动化学报, 2024, 50(4): 1−11 doi: 10.16383/j.aas.c220635
Lu Jin, Ma Ling-Kun, Lv Chun-Ling, Zhang Wei-Chuan, Sun Chang-Ming. A multi-target track-before-detect algorithm based on cost-reference particle filter bank. Acta Automatica Sinica, 2024, 50(4): 1−11 doi: 10.16383/j.aas.c220635
Citation: Lu Jin, Ma Ling-Kun, Lv Chun-Ling, Zhang Wei-Chuan, Sun Chang-Ming. A multi-target track-before-detect algorithm based on cost-reference particle filter bank. Acta Automatica Sinica, 2024, 50(4): 1−11 doi: 10.16383/j.aas.c220635

基于代价参考粒子滤波器组的多目标检测前跟踪算法

doi: 10.16383/j.aas.c220635
基金项目: 国家自然科学基金(61801281)资助
详细信息
    作者简介:

    卢锦:陕西科技大学电子信息与人工智能学院讲师. 主要研究方向为目标检测与跟踪.E-mail: lj491216@163.com

    马令坤:陕西科技大学电子信息与人工智能学院教授. 主要研究方向为数字信号处理. 本文通信作者.E-mail: malingkun@sust.edu.cn

    吕春玲:施耐德(西安)创新技术有限公司工程师. 主要研究方向为数字信号处理. E-mail: chunling.lv@se.com

    章为川:格里菲斯大学集成与智能系统研究所研究员. 主要研究方向为图像信号处理. E-mail: zwc2003@163.com

    SUN Chang-Ming:联邦科学与工业研究组织Data61中心研究员. 主要研究方向为图像信号处理. E-mail: changming.sun@csiro.au

A Multi-target Track-before-detect Algorithm Based on Cost-reference Particle Filter Bank

Funds: Supported by National Natural Science Foundation of China (61801281)
More Information
    Author Bio:

    LU Jin Lecturer at the School of Electrical Information and Artificial Intelligence, Shaanxi University of Science & Technology. Her main research interest is target detection and tracking

    MA Ling-Kun Professor at the School of Electrical Information and Artificial Intelligence, Shaanxi University of Science & Technology. His main research interest is digital signal processing. Corresponding author of this paper

    LV Chun-Ling Engineer at Schneider (Xi'an) Innovation & Technology Company Limited. Her main research interest is digital signal processing

    ZHANG Wei-Chuang Professor at the Institute of Integrated and Intelligence Systems, Griffith University. His main research interest is image signal processing

    SUN Chang-Ming Professor at the Data61, Commonwealth Scien-tific and Industrial Research Organization. His main research interest is image signal processing

  • 摘要: 现有从图像序列中检测和跟踪低信噪比、时变数量多目标方法, 将多目标视为一个整体. 因此, 随着目标数量的增加, 会出现算法结构复杂、计算量增大、性能下降等问题. 针对上述问题, 提出一种基于代价参考粒子滤波器组(Cost-reference particle filter bank, CRPFB)的多目标检测前跟踪(Cost-reference particle filter bank based multi-target track-before-detect, CRPFB-MTBD)算法, 将多目标跟踪问题转换为序贯地检测和估计多个单目标的问题. 首先, 采用代价参考粒子滤波器组序贯地估计所有可能单目标状态序列; 其次, 基于欧氏距离, 合并或删减多个单目标状态, 确定目标数量; 最后, 根据累积代价, 判断每个目标出现和消失的具体时刻. 仿真实验验证了CRPFB-MTBD的优良性能, 与基于传统粒子滤波的多目标检测前跟踪算法(Particle filter based multi-target track-before-detect, PF-MTBD)、基于概率假设密度的检测前跟踪算法(Probability hypothesis density based track-before-detect, PHD-TBD)和基于伯努利滤波的检测前跟踪算法(Bernoulli based track-before-detect, Bernoulli-TBD)相比, CRPFB-MTBD的目标状态和数量估计结果最佳, 且平均单次运行时间极短.
  • 图  1  CRPFB的基本结构

    Fig.  1  Basic structure of CRPFB

    图  2  原始先验信息与表1先验信息的对比

    Fig.  2  Comparison of original prior information and the prior information in table 1

    图  3  基于CRPFB-MTBD算法基本框架

    Fig.  3  Basic structure of CRPFB-MTBD

    图  4  估计可能的目标航迹

    Fig.  4  Estimated trajectories of possible targets

    图  5  估计目标数量

    Fig.  5  Estimation of target number

    图  6  判断各个目标存在的具体时刻

    Fig.  6  Determine the specific moments of each target existing

    图  7  当SNR = 10 dB时的一次观测

    Fig.  7  One observation when SNR = 10 dB

    图  8  当SNR = 10 dB, 3个目标时, 4种方法的OSPA

    Fig.  8  Comparison of OSPAs resulted from 4 algorithms when SNR = 10 dB and 3 targets

    图  9  当SNR = 8dB, 3个目标时, 4种方法的OSPA

    Fig.  9  Comparison of OSPAs resulted from 4 algorithms when SNR = 8 dB and 3 targets

    图  10  当SNR = 8 dB, 3个目标时, CRPFB-MTBD的目标状态估计结果

    Fig.  10  State estimation of CRPFB-MTBD when SNR = 8 dB and 3 targets

    图  11  当SNR = 8 dB, 3个目标时, CRPFB-MTBD的目标数量估计结果

    Fig.  11  Estimation of target number provided by CRPFB-MTBD when SNR = 8 dB and 3 targets

    图  12  当SNR = 6 dB时, 目标数量对CRPFB-MTBD性能的影响

    Fig.  12  Impact of target number on CRPFB-MTBD when SNR = 6 dB

    图  13  当SNR = 6 dB, 3个目标时, CRPF数量对CRPFB-MTBD性能的影响

    Fig.  13  Impact of the number of CRPFs on CRPFB-MTBD when SNR = 6 dB and 3 targets

    图  14  当SNR = 6dB, 5个目标时, CRPF数量对CRPFB-MTBD性能的影响

    Fig.  14  Impact of the number of CRPFs on CRPFB-MTBD when SNR = 6 dB and 5 targets

    图  15  当SNR = 6 dB, 3个目标时, 门限$ V_{2}$对CRPFB-MTBD性能的影响

    Fig.  15  Impact of threshold $ V_{2}$ on CRPFB-MTBD when SNR = 6dB and 3 targets

    图  16  当SNR = 6 dB, 5个目标时, 门限$ V_{2}$对CRPFB-MTBD性能的影响

    Fig.  16  Impact of threshold $ V_{2}$ on CRPFB-MTBD when SNR = 6dB and 5 targets

    表  1  第$l$个目标的先验信息

    Table  1  Initial information for the $l$th target

    先验信息x方向y方向
    初始位置$x_{l,1}=x_{m_{s}}$$y_{l,1}=y_{m_{s}}$
    速度范围$\dfrac{-x_{m_{s}}}{K\triangle T}\leq \dot{x}_{l}\leq \dfrac{N\triangle_{x}-x_{m_{s}}}{K\triangle T}$$\dfrac{-y_{m_{s}}}{K\triangle T}\leq \dot{y}_{l}\leq \dfrac{M\triangle_{y}-y_{m_{s}}}{K\triangle{T}}$
    $k$时刻位置范围$x_{m_{s}}-(k-1)\dfrac{-x_{m_{s}}}{K\triangle T} \leq x_{m_{s}}+(k-1)\dfrac{N\triangle_{x}-x_{m_{s}}}{K\triangle T}$$y_{m_{s}}-(k-1)\dfrac{-y_{m_{s}}}{K\triangle T} \leq y_{m_{s}}+(k-1)\dfrac{M\triangle_{y}-y_{m_{s}}}{K\triangle T}$
    下载: 导出CSV

    表  2  4种算法的平均单次运行时间 (s)

    Table  2  Average single running time of 4 algorithms (s)

    算法运行时间
    PHD-TBD506.8180
    PF-MTBD131.0574
    Bernoulli-TBD6.6079
    CRPFB-MTBD0.0116
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-11
  • 录用日期:  2023-02-23
  • 网络出版日期:  2023-04-24

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