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基于星凸形随机超曲面模型多扩展目标多伯努利滤波器

陈辉 杜金瑞 韩崇昭

陈辉, 杜金瑞, 韩崇昭. 基于星凸形随机超曲面模型多扩展目标多伯努利滤波器. 自动化学报, 2020, 46(5): 909-922. doi: 10.16383/j.aas.c180130
引用本文: 陈辉, 杜金瑞, 韩崇昭. 基于星凸形随机超曲面模型多扩展目标多伯努利滤波器. 自动化学报, 2020, 46(5): 909-922. doi: 10.16383/j.aas.c180130
CHEN Hui, DU Jin-Rui, HAN Chong-Zhao. A Multiple Extended Target Multi-Bernouli Filter Based on Star-convex Random Hypersurface Model. ACTA AUTOMATICA SINICA, 2020, 46(5): 909-922. doi: 10.16383/j.aas.c180130
Citation: CHEN Hui, DU Jin-Rui, HAN Chong-Zhao. A Multiple Extended Target Multi-Bernouli Filter Based on Star-convex Random Hypersurface Model. ACTA AUTOMATICA SINICA, 2020, 46(5): 909-922. doi: 10.16383/j.aas.c180130

基于星凸形随机超曲面模型多扩展目标多伯努利滤波器

doi: 10.16383/j.aas.c180130
基金项目: 

国家自然科学基金 61873116

国家自然科学基金 51668039

甘肃省科技计划项目 18YF1GA065

甘肃省科技计划项目 18JR3RA137

国防基础科研项目 JCKY2018427C002

详细信息
    作者简介:

    杜金瑞  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: djr62@sina.com

    韩崇昭  西安交通大学电子与信息工程学院教授.主要研究方向为多源信息融合, 随机控制与自适应控制, 非线性频谱分析. E-mail: czhan@mail.xjtu.edu.cn

    通讯作者:

    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

A Multiple Extended Target Multi-Bernouli Filter Based on Star-convex Random Hypersurface Model

Funds: 

National Natural Science Foundation of China 61873116

National Natural Science Foundation of China 51668039

Gansu Provincial Science and Technology Planning of China 18YF1GA065

Gansu Provincial Science and Technology Planning of China 18JR3RA137

National Defense Basic Research Project JCKY2018427C002

More Information
    Author Bio:

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

    HAN Chong-Zhao  Professor at the School of Electronic and Information Engineering, Xi'an Jiaotong University. His research interest covers multi-source information fusion, stochastic control and adaptive control, and nonlinear spectral analysis

    Corresponding author: CHEN Hui  Professor at the School of Electrical and Information Engineering, Lanzhou University of Technology. His research interest covers target tracking and sensor management. Corresponding author of this paper
  • 摘要: 针对复杂不确定性环境下具有不规则形状的多扩展目标跟踪问题, 提出了一种基于星凸形随机超曲面模型(Star-convex RHM)的多扩展目标多伯努利滤波算法.首先, 在有限集统计(Finite set statistics, FISST)理论框架下, 采用多伯努利随机有限集(MBer-RFS)和泊松RFS (Possion-RFS)分别描述多扩展目标的状态和观测, 并给出扩展目标势均衡多目标多伯努利(ET-CBMeMBer)滤波器.其次, 利用RHM去描述任意星凸形扩展目标的量测源分布, 提出了容积卡尔曼高斯混合星凸形多扩展目标多伯努利滤波器.此外, 本文给出了一种多扩展目标不规则形状估计性能的评价指标.最后, 通过多扩展目标和具有形状突变的多群目标的跟踪仿真实验验证了本文方法的有效性.
    Recommended by Associate Editor GUO Ge
    1)  本文责任编委 郭戈
  • 图  1  星凸形的径向函数描述

    Fig.  1  Radius function representation of star-convex shape

    图  2  星凸形扩展目标的随机超曲面模型

    Fig.  2  RHM of extended target with star-convex shape

    图  3  目标真实运动轨迹

    Fig.  3  Actual target trajectories

    图  4  扩展目标形状及量测分布

    Fig.  4  Shape and measurements of the extended target

    图  5  两种滤波器对多扩展目标的跟踪效果图

    Fig.  5  The tracking result of the two filters (ET)

    图  6  两种滤波器的形状估计局部放大图

    Fig.  6  The partial enlarged effect of the two filters for shape estimation (ET)

    图  7  两种滤波器下扩展目标的势估计

    Fig.  7  Cardinality estimation of the two filters (ET)

    图  8  扩展目标质心位置估计的OSPA

    Fig.  8  OSPA statistics of the centroid position estimation (ET)

    图  9  扩展目标形状估计的拟Jaccard距离

    Fig.  9  Quasi-Jaccard distance of the shape estimation (ET)

    图  10  群目标形状及量测分布

    Fig.  10  Shape and measurements of the group target

    图  11  两种滤波器对群目标的跟踪效果图

    Fig.  11  The tracking result of the two filters (GT)

    图  12  两种滤波器的对群目标的形状估计局部放大图

    Fig.  12  The partial enlarged effect of the two filters for shape estimation (GT)

    图  13  两种滤波器下群目标的势估计

    Fig.  13  Cardinality estimation statistics of the two filters (GT)

    图  14  群目标位置估计的OSPA

    Fig.  14  OSPA statistics of the position estimation (GT)

    图  15  群目标形状估计的拟Jaccard距离

    Fig.  15  Quasi-Jaccard distance of the shape estimation (GT)

    表  1  多目标初始参数

    Table  1  Initial parameters of multi-target

    目标 新生时刻(s) 消亡时刻(s) 位置(m) 速度(m/s)
    目标1 1 35 $[10, -50]^{\rm T}$ $[10, 2]^{\rm T}$
    目标2 11 50 $[10, 10]^{\rm T}$ $[8, 5]^{\rm T}$
    目标3 26 50 $[10, 50]^{\rm T}$ $[12, 2]^{\rm T}$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-03-06
  • 录用日期:  2018-07-23
  • 刊出日期:  2020-06-01

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