分布式多传感器多目标跟踪方法综述

曾雅俊; 王俊; 魏少明; 孙进平; 雷鹏

doi:10.12000/JR22111

分布式多传感器多目标跟踪方法综述

DOI: 10.12000/JR22111

北京航空航天大学电子信息工程学院北京 100191

基金项目: 国家自然科学基金(62171029, 61671035)，预研基金(61404130122)，重点实验室基金(6142502180103)，教育部产学合作协同育人项目(202101105001)

详细信息

作者简介:
曾雅俊，博士生，主要研究方向为多目标跟踪、多源信息融合

王　俊，博士，教授，主要研究方向为雷达信号处理、FPGA/DSP嵌入式系统、目标识别与跟踪、多传感器数据融合

魏少明，博士，实验师，主要研究方向为雷达信号处理、多目标跟踪、数据融合、三维成像

孙进平，教授，博士生导师，主要研究方向为目标跟踪、信号分析检测与估计、稀疏微波成像、图像理解、雷达信号与数据处理的算法及软硬件实现

雷　鹏，博士，副教授，硕士生导师，主要研究方向为数字信号处理、贝叶斯估计、模式识别

通讯作者:
魏少明 shaoming.wei@buaa.edu.cn

责任主编：关键 Corresponding Editor: GUAN Jian
中图分类号: TN951; TN957.51; TN971.⁺1
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出版历程
- 收稿日期: 2022-06-08
- 修回日期: 2022-08-02
- 网络出版日期: 2022-08-15
- 刊出日期: 2023-02-28

Review of the Method for Distributed Multi-sensor Multi-target Tracking

School of Electronic and Information Engineering, Beihang University, Beijing 100191, China

Funds: The National Natural Science Foundation of China (62171029, 61671035), The Pre-research Foundation (61404130122), The Key Laboratory Foundation (6142502180103), The Ministry of Education’s Industry-University Cooperation and Collaborative Education Project (202101105001)

More Information

Corresponding author: WEI Shaoming, shaoming.wei@buaa.edu.cn

摘要

摘要: 多传感器多目标跟踪是信息融合领域的热点问题，其通过融合多个局部传感器数据，提高目标跟踪精度和稳定性。多传感器多目标跟踪按融合体系可分为分布式、集中式、混合式3类，其中分布式融合结构对网络通信带宽要求低、可靠性和稳定性强，广泛应用于军事、民用领域。该文聚焦分布式多传感器多目标跟踪涉及的目标跟踪、传感器配准、航迹关联、数据融合4项关键技术，主要分析了各关键技术的理论原理与适用条件，重点介绍了不完整测量条件下的空间配准与航迹关联，并给出仿真结果。最后，该文总结了现有分布式多传感器多目标跟踪关键技术存在的问题，并指出了其未来发展趋势。
- 分布式多传感器多目标跟踪 /
- 目标跟踪 /
- 传感器配准 /
- 航迹关联 /
- 数据融合
Abstract: Multi-sensor multi-target tracking is a popular topic in the field of information fusion. It improves the accuracy and stability of target tracking by fusing multiple local sensor information. By the fusion system, the multi-sensor multi-target tracking is grouped into distributed fusion, centralized fusion, and hybrid fusion. Distributed fusion is widely applied in the military and civilian fields with the advantages of strong reliability, high stability, and low requirements on network communication bandwidth. Key techniques of distributed multi-sensor multi-target tracking include multi-target tracking, sensor registration, track-to-track association, and data fusion. This paper reviews the theoretical basis and applicable conditions of these key techniques, highlights the incomplete measurement spatial registration algorithm and track association algorithm, and provides the simulation results. Finally, the weaknesses of the key techniques of distributed multi-sensor multi-target tracking are summarized, and the future development trends of these key techniques are surveyed.
- Distributed Multi-sensor Multi-target Tracking (DMMT) /
- Multi-target tracking /
- Sensor registration /
- Track-to-track association /
- Data fusion

HTML全文

图 1 分布式多传感器多目标跟踪流程图

Figure 1. Flowchart of distributed multi-sensor multi-target tracking

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图 2 时间配准方法

Figure 2. Time registration methods

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图 3 空间配准几何示意图

Figure 3. Illustration of spatial registration

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图 4 空间配准场景

Figure 4. Scene of spatial registration

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图 5 WGS84坐标系下配准前后显著性目标位置^[82]

Figure 5. Registration results before and after registration in the WGS84 coordinate system^[82]

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图 6 典型的航迹关联方法及分类

Figure 6. Classification of track-to-track association methods

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图 7 航迹关联场景

Figure 7. Scene of track-to-track association

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图 8 航迹关联正确率^[82]

Figure 8. Accuracy of track association^[82]

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图 9 航迹关联与空间配准关系^[82]

Figure 9. Relationship of track-track association and spatial registration^[82]

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图 10 分布式多传感器估计融合

Figure 10. Distributed multi-sensor estimation fusion

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表 1 典型的多目标跟踪方法性能对比

Table 1. Performance comparison of different multi-target tracking methods

多目标跟踪类型	跟踪方法	跟踪精度	运算量
数据关联类	GNN	低	低
	JPDA	中等	中等
	JIPDA	中等	中等
	MHT	高	高
随机有限集类	PHD	低	低
	CPHD	中等	中等
	TPHD	中等	低
	TCPHD	中等	中等
	MeMBer	中等	中等
	PMBM	高	中等
	GLMB	高	高
	LMB	高	中等

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表 2 多传感器时间配准方法性能对比

Table 2. Comparison of multi-sensor time registration methods

配准类型	配准方法	配准精度	计算量	目标运动状态
插值类	内插外推	较低	低	匀速
	曲线插值	中等	较高	匀速\非匀速
	曲线拟合	中等	较高	匀速\非匀速
参数估计类	最小二乘	中等	中等	匀速
参数估计类	卡尔曼滤波	较高	较高	匀速\非匀速

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表 3 多传感器非合作目标空间配准方法分类

Table 3. Classification of multi-sensor spatial registration methods based on non-cooperative targets

配准方法	实时性	是否能估计目标位置	参与传感器个数	传感器类型
RTQC	离线	否	两个	同类
LS	离线	否	两个	同类
GLS	离线	否	两个	同类
EML	离线	是	两个	同类
KF	在线	是	多个	同类
RFS	在线	是	多个	同类
MLR	离线	是	多个	同类\异类
RBER	离线	是	多个	同类\异类

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表 4 航迹关联性能对比

Table 4. Comparison of multi-sensor track-to-track association methods

航迹关联方法	航迹关联正确率(%)	时间开销(s)
SMBTANTD^[82]	99.6	3.4
Generalized Likelihood^[15]	97.4	2.8
Fuzzy function^[89]	96.7	8.1

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表 5 多传感器估计融合方法对比

Table 5. Comparison of multi-sensor estimation fusion methods

估计融合方法	是否考虑航迹相关	计算量	融合精度	传感器类型
简单凸组合融合	否	低	低	同类
Bar-Shalom-Campo融合	是	较高	较高	同类
基于MAP	是	较高	较高	同类
CI融合	是	较高	较高	同类
GCI融合	是	较高	较高	同类/异类
AA融合	是	较高	较高	同类/异类
基于EKF融合	否	较高	较高	同类/异类
基于UKF融合	否	较高	较高	同类/异类
基于PF融合	否	高	高	同类/异类

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表 6 典型的多传感器多目标跟踪方法性能对比

Table 6. Performance comparison of multi-sensor multi-target tracking methods

多目标跟踪类型	融合准则	跟踪方法	运算量	跟踪精度
数据关联类	CI融合	JPDA	中等	中等
数据关联类	简单凸组合融合	MHT	高	中等
随机有限集类	GCI/GA融合	PHD	低	低
		CPHD	中等	中等
		MeMBer	中等	中等
		GLMB	高	高
		LMB	中等	高
	AA融合	PHD	低	低
		CPHD	中等	中等
		MeMBer	中等	中等
		GLMB	高	高
		LMB	中等	高

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