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群目标跟踪技术综述

甘林海 王刚 刘进忙 李松

甘林海, 王刚, 刘进忙, 李松. 群目标跟踪技术综述. 自动化学报, 2020, 46(3): 411-426. doi: 10.16383/j.aas.c180052
引用本文: 甘林海, 王刚, 刘进忙, 李松. 群目标跟踪技术综述. 自动化学报, 2020, 46(3): 411-426. doi: 10.16383/j.aas.c180052
GAN Lin-Hai, WANG Gang, LIU Jin-Mang, LI Song. An Overview of Group Target Tracking. ACTA AUTOMATICA SINICA, 2020, 46(3): 411-426. doi: 10.16383/j.aas.c180052
Citation: GAN Lin-Hai, WANG Gang, LIU Jin-Mang, LI Song. An Overview of Group Target Tracking. ACTA AUTOMATICA SINICA, 2020, 46(3): 411-426. doi: 10.16383/j.aas.c180052

群目标跟踪技术综述

doi: 10.16383/j.aas.c180052
基金项目: 

国家自然科学基金青年基金 61703412

国家自然科学基金青年基金 61503407

详细信息
    作者简介:

    王刚  空军工程大学防空反导学院教授.主要研究方向为多传感器任务规划. E-mail: profwang123@163.com

    刘进忙  空军工程大学防空反导学院教授.主要研究方向为目标跟踪, 多传感数据融合. E-mail:liujinmang1@163.com

    李松  空军工程大学防空反导学院副教授.主要研究方向为模式识别及数据融合.E-mail: lisong77@163.com

    通讯作者:

    甘林海  空军工程大学博士研究生.主要研究方向群目标跟踪.本文通信作者. E-mail: ganlh123@163.com

An Overview of Group Target Tracking

Funds: 

National Natural Science Foundation of Youth Fund of China 61703412

National Natural Science Foundation of Youth Fund of China 61503407

More Information
    Author Bio:

    WANG Gang  Professor at the College of Air and Missile Defense, Air Force Engineering University. His main research interest is multi-sensor planning

    LIU Jin-Mang  Professor at the College of Air and Missile Defense, Air Force Engineering University. His research interest covers targets tracking and multi-sensor data fusion

    LI Song  Associate professor at the College of Air and Missile Defense, Air Force Engineering University. His research interest covers pattern recognition and data fusion

    Corresponding author: GAN Lin-Hai Ph. D. candidate at Air Force Engineering University. His main research interest is group target tracking. Corresponding author of this paper
  • 摘要: 根据研究重点的不同, 从群目标跟踪的建模和滤波算法方面展开综述, 主要包括:量测处理、扩展外形建模、内部关系建模、群分裂/合并建模以及滤波算法等.最后, 基于群目标跟踪现有研究进展和未来可能面临的挑战, 对群目标跟踪领域需要重点研究和关注的方向作了展望.
    Recommended by Associate Editor ZHU Ji-Hong
    1)  本文责任编委 朱纪洪
  • 图  1  群目标跟踪技术主要研究内容

    Fig.  1  Main research contents of group target tracking

    图  2  不同扩展外形的群目标示意图

    Fig.  2  Group targets with different extended shapes

    图  3  多椭圆建模任意外形群目标示意图

    Fig.  3  Modeling arbitrary shape group targets with multiple ellipse center

    图  4  边更新模型示意图

    Fig.  4  Model of edge renewal

    图  5  新顶点合并模型示意图

    Fig.  5  Model of new vertex merging

    图  6  可分辨群的分裂/合并示意图

    Fig.  6  Splitting/combination of resolvable groups

    图  7  不可分辨群的分裂/合并示意图

    Fig.  7  Splitting/combination of unresolvable groups

    表  1  常见量测划分方法及其特点

    Table  1  Common partitioning methods and their characteristics

    划分方法 特点
    距离划分[17, 29] 应用广泛, 不能处理空间临近群目标划分问题
    距离子划分[17, 20] 距离划分方法的改进, 可用于处理空间临近但大小相同的群目标划分问题
    预测划分和期望最大划分[22] 考虑了目标扩展外形, 但在多个空间临近群分离的场景容易造成势低估
    基于ART的系列方法[24-27] 较好的划分效率和稳定性, 易出现"饱和"问题
    下载: 导出CSV

    表  2  常见群扩展外形的主要建模方法及特点

    Table  2  Main modeling methods and features of common group extensions

    扩展外形 建模方法 特点
    椭圆外形 RM方法[14, 44, 55-57] 波方程简单, 预设先验参数少、鲁棒性强, 适用于线性系统
    RHM方法[43-44] 精度高, 比RM方法计算复杂
    矩形外形 矩形参数法[11] 波方程简单, 预设先验参数少、鲁棒性强, 适用于线性系统
    区间箱方法[46] 密集杂波条件下效果较好; 只描述了量测外形而没有估计目标外形
    星凸外形 星凸RHM方法[20, 58] 比简单外形建模方法具有更高的跟踪精度和更强的鲁棒性, 可描述更复杂的群外形; 估计半径可能为负
    GP方法[49, 59] 半径函数具有描述后验分布的能力; 不需要进行频域转换; 能保持不可观测部分的不确定性; 能够估计目标方向; 估计半径可能为负
    任意外形 水平集RHM方法[40] 能够在信息不确定条件下对时变外形进行高精度跟踪; 初始化困难; 需要正则化以满足鲁棒性要求; 需要处理强噪声造成的估计偏差
    多椭圆方法[12-13, 16, 52] 利用多个简单外形描述复杂外形, 具有较高的灵活性, 算法复杂度随着子目标数目的增加而增加
    下载: 导出CSV

    表  3  常见群内部关系模型及特点

    Table  3  Common group internal relationship models and their characteristics

    模型 特点
    虚拟领导者模型[60, 72] 用于线性高斯系统; 可能出现目标的碰撞或重叠
    MRF模型[61] 可以描述群内目标之间的复杂行为; 闭合表达式中的归一化常量通常是未知的; 没有考虑目标遮挡造成的航迹丢失问题[70]
    演化网络模型[64] 引入随机图模型, 利用图中的节点表示群成员, 边表示群成员之间的相互关系, 可以通过增加或删减节点改变图的大小, 节点之间边的产生具有选择性; 没有对群内目标重要性作出区分
    社会力模型[66-70, 73] 主要用于人群跟踪, 有一个明确的目的吸引群内成员相向运动, 而其他成员或静态障碍的阻碍则产生反响斥力[74]
    下载: 导出CSV

    表  4  群分裂/合并模型

    Table  4  Group splitting/combination model

    下载: 导出CSV
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  • 收稿日期:  2018-01-22
  • 录用日期:  2018-08-17
  • 刊出日期:  2020-03-30

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