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交互式箱粒子标签多伯努利机动目标跟踪算法

蔡如华 杨标 吴孙勇 孙希延

蔡如华, 杨标, 吴孙勇, 孙希延. 交互式箱粒子标签多伯努利机动目标跟踪算法. 自动化学报, 2020, 46(11): 2448-2460 doi: 10.16383/j.aas.c180069
引用本文: 蔡如华, 杨标, 吴孙勇, 孙希延. 交互式箱粒子标签多伯努利机动目标跟踪算法. 自动化学报, 2020, 46(11): 2448-2460 doi: 10.16383/j.aas.c180069
Cai Ru-Hua, Yang Biao, Wu Sun-Yong, Sun Xi-Yan. Interacting multiple model box-LMB target tracking algorithm. Acta Automatica Sinica, 2020, 46(11): 2448-2460 doi: 10.16383/j.aas.c180069
Citation: Cai Ru-Hua, Yang Biao, Wu Sun-Yong, Sun Xi-Yan. Interacting multiple model box-LMB target tracking algorithm. Acta Automatica Sinica, 2020, 46(11): 2448-2460 doi: 10.16383/j.aas.c180069

交互式箱粒子标签多伯努利机动目标跟踪算法

doi: 10.16383/j.aas.c180069
基金项目: 

国家自然科学基金 61561016

广西自然科学基金 2016GXNSFAA380073

广西密码学信息安全重点实验室研究课题项目 GCIS201611

详细信息
    作者简介:

    蔡如华   桂林电子科技大学数学与计算科学学院副教授.主要研究方向为小波分析, 信号处理, 粒子滤波. E-mail:ruhuac@guet.edu.cn

    杨标   桂林电子科技大学数学与计算科学学院硕士研究生.主要研究方向为微弱目标检测与跟踪, 粒子滤波. E-mail:13677736552@163.com

    孙希延   桂林电子科技大学信息与通信工程学院教授.主要研究方向为卫星通信, 卫星导航. E-mail: sunxiyan1@163.com

    通讯作者:

    吴孙勇   桂林电子科技大学数学与计算科学学院副教授.主要研究方向为微弱目标检测与跟踪, 阵列信号处理, 粒子滤波.本文通信作者. E-mail: wusunyong121991@163.com

Interacting Multiple Model Box-LMB Target Tracking Algorithm

Funds: 

National Natural Science Foundation of China 61561016

Guangxi National Natural Science Foundation of China 2016GXNSFAA380073

Guangxi Key Laboratory of Cryptography and Information Security GCIS201611

More Information
    Author Bio:

    CAI Ru-Hua    Associate professor at the School of Mathematics and Computational Science, Guilin University of Electronic Technology. His research interest covers wavelet analysis, signal processing and particle filter

    YANG Biao    Master student at the School of Mathematics and Computational Science, Guilin University of Electronic Technology. His research interest covers weak target tracking and detection, particle filter

    SUN Xi-Yan    Professor at the School of Information and Communication Engineering, Guilin University of Electronic Technology. Her research interest covers satellite communications, navigation satellite

    Corresponding author: WU Sun-Yong   Associate professor at the School of Mathematics and Computational Science, Guilin University of Electronic Technology. His research interest covers weak target tracking and detection, array signal processing, particle filter. Corresponding author of this paper
  • 摘要: 针对多机动目标追踪问题, 将交互式多模型(Interacting multiple model, IMM)思想与箱粒子标签多伯努利滤波器(Box-labeled multi-bernoulli filter, Box-LMB)相结合, 提出交互式箱粒子标签多伯努利滤波器(IMM-Box-LMB)算法.该算法首先通过扩展多目标状态, 引入模型匹配概率变量, 并利用量测信息在预测阶段更新模型匹配概率, 进而使用交互式多模型算法对每个箱粒子状态进行混合估计.其次, 在更新阶段提出二次收缩算法, 通过二次收缩算法使更新后的箱粒子具有更大的区间和存活概率, 也更加接近真实目标位置, 从而达到提升后续时刻箱粒子多样性的目的.仿真结果表明, 二次收缩算法能够有效地提升箱粒子的多样性.将二次收缩算法应用于IMM-Box-LMB算法, 能够在不同信噪比下稳定准确地估计机动目标的个数.相同条件下, 与匀速直线运动(Constant velocity, CV)模型下的Box-LMB算法相比, IMM-Box-LMB算法能够对多机动目标的数目以及状态进行更加有效的估计.
    Recommended by Associate Editor LIU Yun-Gang
    1)  本文责任编委  刘允刚
  • 图  1  二次收缩

    Fig.  1  Secondary contraction

    图  2  传统收缩算法和二次收缩算法下箱粒子状态更新

    Fig.  2  The box particle state update by using the traditional contraction algorithm and the quadratic contraction algorithm, respectively

    图  3  量测

    Fig.  3  Measure

    图  4  IMM-Box-LMB算法对目标状态估计与目标航迹跟踪效果

    Fig.  4  True trajectories and estimates of targets using the IMM-Box-LMB algorithm

    图  5  IMM-Box-LMB算法使用二次收缩算法和传统收缩算法更新之后的有效箱粒子数的比值(50 MC)

    Fig.  5  Count the number of effective box particles for IMM-Box-LMB algorithm using the quadratic contraction algorithm and the traditional contraction algorithm, respectively, and then return to their ratio (50 MC)

    图  6  IMM-Box-LMB算法使用二次收缩算法和传统收缩算法对多机动目标的势估计(50 MC)

    Fig.  6  Cardinality statistics returned by IMM-Box-LMB algorithm using the traditional contraction algorithm and the quadratic contraction algorithm, respectively (50 MC)

    图  7  IMM-Box-LMB算法使用二次收缩算法和传统收缩算法之后(a) OSPA距离和OSPA成分(b)位置和(c)势估计$p = 1$, $c = 5$ (50 MC)

    Fig.  7  The OSPA of IMM-Box-LMB algorithm using the traditional contraction algorithm and the quadratic contraction algorithm, respectively. (a) OSPA distance and OSPA components (b) localization component (c) cardinality component $p=1$, $c = 5$ (50MC)

    图  8  IMM-Box-LMB, CV-Box-LMB不同杂波率下目标个数估计(50 MC)

    Fig.  8  IMM-Box-LMB, CV-Box-LMB cardinality estimates under different clutter rates (50 MC)

    图  9  不同杂波率下IMM-Box-LMB算法和CV-Box-LMB算法OSPA距离(a)和OSPA成分位置(b)和势(c)估计$p = 1$, $c = 5$ (50 MC)

    Fig.  9  Under different clutter rates, (a) The OSPA distance and OSPA components. (b) localization component (c) cardinality component $(p = 1, c = 5)$ using IMM-Box-LMB algorithm and CV-Box-LMB algorithm, respectively (50 MC)

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出版历程
  • 收稿日期:  2018-01-27
  • 录用日期:  2018-07-02
  • 刊出日期:  2020-11-24

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