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基于多伯努利滤波的厚尾噪声条件下多扩展目标跟踪

陈辉 张星星

陈辉, 张星星. 基于多伯努利滤波的厚尾噪声条件下多扩展目标跟踪. 自动化学报, 2021, 45(x): 1−14 doi: 10.16383/j.aas.c201061
引用本文: 陈辉, 张星星. 基于多伯努利滤波的厚尾噪声条件下多扩展目标跟踪. 自动化学报, 2021, 45(x): 1−14 doi: 10.16383/j.aas.c201061
CHEN Hui, Zhang Xing-Xing. Multiple extended target tracking in the presence of heavy tailed noise using multi-Bernoulli filtering method. Acta Automatica Sinica, 2021, 45(x): 1−14 doi: 10.16383/j.aas.c201061
Citation: CHEN Hui, Zhang Xing-Xing. Multiple extended target tracking in the presence of heavy tailed noise using multi-Bernoulli filtering method. Acta Automatica Sinica, 2021, 45(x): 1−14 doi: 10.16383/j.aas.c201061

基于多伯努利滤波的厚尾噪声条件下多扩展目标跟踪

doi: 10.16383/j.aas.c201061
基金项目: 国防基础科研项目(JCKY2018427C002), 国家自然科学基金(61873116, 61763029), 甘肃省科技计划项目(20JR10RA184)资助
详细信息
    作者简介:

    陈辉:兰州理工大学电气工程与信息工程学院教授. 主要研究方向为目标跟踪和传感器管理. 本文通信作者. E-mail: huich78@hotmail.com

    张星星:兰州理工大学电气工程与信息工程学院硕士研究生. 主要研究方向为扩展目标跟踪. E-mail: zhangxing_59@163.com

Multiple Extended Target Tracking in the Presence of Heavy Tailed Noise Using Multi-bernoulli Filtering Method

Funds: Supported by National Defense Basic Research Project of China (JCKY2018427C002), National Natural Science Foundation of China (61873116, 61763029), Gansu Provincial Science and Technology Planning (20JR10RA184)
More Information
    Author Bio:

    CHEN Hui Professor at the School of Electrical and Information Engineering, Lanzhou University of Technology. His main research interest is target tracking and sensor management

    ZHANG Xing-Xing Master student at the School of Electrical and Information Engineering, Lanzhou University of Technology. Her main research interest is extended target tracking

  • 摘要: 针对厚尾噪声条件下不规则星凸形多扩展目标跟踪问题, 本文提出了一种基于多伯努利滤波的厚尾噪声条件下多扩展目标跟踪方法. 首先, 采用学生t分布对厚尾过程噪声和量测噪声进行建模, 并基于有限集统计理论(Finite set statistics, FISST)利用随机超曲面模型(Random matrix model, RHM)建立不规则星凸形多扩展目标的跟踪滤波模型. 然后, 利用学生t混合(Student's t mixture, STM)模型来表征多伯努利密度, 提出学生t混合多扩展目标多伯努利滤波算法, 并进一步基于鲁棒学生t容积滤波算法提出了非线性鲁棒学生t混合星凸形多扩展目标多伯努利滤波算法. 最后, 通过构造厚尾噪声条件下星凸形多扩展目标和多群目标的跟踪仿真实验验证了所提方法的有效性.
  • 图  1  高斯分布和学生t分布示意图

    Fig.  1  Gaussian distribution and student's t distribution diagram

    图  2  多扩展目标的跟踪效果图

    Fig.  2  The tracking result of multiple extended targets

    图  3  形状估计局部放大效果图

    Fig.  3  The partial enlarged effect for shape estimation(ET)

    图  4  多扩展目标的势估计图

    Fig.  4  Cardinality estimation of multiple extended targets

    图  5  多扩展目标质心位置估计的OSPA

    Fig.  5  OSPA statistics of the centroid position estimation (ET)

    图  6  扩展目标形状估计的拟Jaccard距离

    Fig.  6  Quasi-Jaccard distance of the shape estimation (ET)

    图  7  群目标的跟踪效果图

    Fig.  7  The tracking result of group targets

    图  8  群目标跟踪形状估计局部放大效果图

    Fig.  8  The partial enlarged effect for shape estimation (GT)

    图  9  群目标跟踪的势估计

    Fig.  9  Cardinality estimation statistics of multiple group targets

    图  10  群目标质心位置估计的OSPA

    Fig.  10  OSPA statistics of the position estimation(GT)

    图  11  群目标形状估计的拟Jaccard距离

    Fig.  11  Quasi-Jaccard distance of the shape estimation(GT)

    表  1  多扩展目标的初始参数

    Table  1  Initial parameters of Multiple extended target

    目标 新生时刻(s) 消亡时刻(s) 初始位置(m) 真实形状(m/s)
    目标1 1 40 $[-200,400]^{\rm T}$ 十字形
    目标2 15 50 $[-400,-400]^{\rm T}$ 十字形
    目标3 25 50 $[400,-200]^{\rm T}$ 五角星形
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  • 收稿日期:  2020-12-24
  • 录用日期:  2021-03-19
  • 网络出版日期:  2021-07-06

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