群目标跟踪技术综述

甘林海; 王刚; 刘进忙; 李松

doi:10.16383/j.aas.c180052

群目标跟踪技术综述

doi: 10.16383/j.aas.c180052

甘林海^1, ,,
王刚^1,,
刘进忙^1,,
李松^1,

1.
空军工程大学防空反导学院西安 710051

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

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出版历程
- 收稿日期: 2018-01-22
- 录用日期: 2018-08-17
- 刊出日期: 2020-03-30

An Overview of Group Target Tracking

GAN Lin-Hai^{1
, ,},
WANG Gang^1
,,
LIU Jin-Mang^1
,,
LI Song^1
,

1.
Air and Missile Defense College, Air Force Engineering University, Xi'an 710051

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

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.
- 群目标 /
- 随机有限集 /
- 随机矩阵 /
- 演化网络模型 /
- 群精细跟踪 /
- 群分裂/合并
Abstract: On the basis of the research focuses of group target tracking, an overview is presented, which includes modeling and filtering. Special attention is paid to the following areas: measurement processing, modeling of extension shape, modeling inter relationships, modeling of group splitting/combination, and filters for group target tracking based on the aforementioned models. Finally, based on the research progress and the challenges that maybe faced in the future, an outlook is made over the key issues that need to be focused on in the field of group target tracking.
- Group target /
- random finite set /
- random matrix /
- evolving networks model /
- group refined tracking /
- group splitting/combination
Recommended by Associate Editor ZHU Ji-Hong
注释:

1) 本文责任编委朱纪洪

HTML全文

图 1 群目标跟踪技术主要研究内容

Fig. 1 Main research contents of group target tracking

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图 2 不同扩展外形的群目标示意图

Fig. 2 Group targets with different extended shapes

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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

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图 4 边更新模型示意图

Fig. 4 Model of edge renewal

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图 5 新顶点合并模型示意图

Fig. 5 Model of new vertex merging

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图 6 可分辨群的分裂/合并示意图

Fig. 6 Splitting/combination of resolvable groups

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图 7 不可分辨群的分裂/合并示意图

Fig. 7 Splitting/combination of unresolvable groups

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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

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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
椭圆外形	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
矩形外形	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
星凸外形	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
任意外形	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

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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]

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表 4 群分裂/合并模型

Table 4 Group splitting/combination model

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