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Review of the Method for Distributed Multi-sensor Multi-target Tracking（in English）
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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。
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关键词:
- 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. -
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
图 5 Registration results before and after registration in the WGS84 coordinate system [ 82]
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 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 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 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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表 1 Performance comparison of different multi-target tracking methods
Types of multiple target tracking Tracking methods Tracking accuracy Computational complexity Data association GNN Low Low JPDA Medium Medium JIPDA Medium Medium MHT High High Random finite set PHD Low Low CPHD Medium Medium TPHD Medium Low TCPHD Medium Medium MeMBer Medium Medium PMBM High Medium GLMB High High LMB High Medium
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表 2 Comparison of multi-sensor time registration methods
Registration types Registration methods Registration accuracy Computational complexity Target motion state Interpolation class Interpolation and extrapolation Lower Lower Uniform Curve interpolation Medium Higher Uniform \ Non-uniform Curve fitting Medium Higher Uniform \ Non-uniform Parameter estimation Least squares Medium Medium Uniform Kalman filter Higher Higher Uniform \ Non-uniform
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表 3 Classification of multi-sensor spatial registration methods based on non-cooperative targets
Registration methods Real-time Can the target position be estimated? Number of sensors Sensor type RTQC Offline No Two Homogeneous LS Offline No Two Homogeneous GLS Offline No Two Homogeneous EML Offline Yes Two Homogeneous KF Online Yes Multiple Homogeneous RFS Online Yes Multiple Homogeneous MLR Offline Yes Multiple Homogeneous \ Heterogeneous RBER Offline Yes Multiple Homogeneous \ Heterogeneous
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表 5 Comparison of multi-sensor estimation fusion methods
Estimation fusion method Whether to consider track correlation Computational
complexityFusion accuracy Sensor type Simple convex combination fusion No Low Low Homogeneous Bar-Shalom-Campo fusion Yes Higher Higher Homogeneous MAP fusion Yes Higher Higher Homogeneous CI fusion Yes Higher Higher Homogeneous GCI fusion Yes Higher Higher Homogeneous \ Heterogeneous AA fusion Yes Higher Higher Homogeneous \ Heterogeneous EKF fusion No Higher Higher Homogeneous \ Heterogeneous UKF fusion No Higher Higher Homogeneous \ Heterogeneous PF fusion No High High Homogeneous \ Heterogeneous
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表 6 Performance comparison of multi-sensor multi-target tracking methods
Types of multiple target tracking Fusion criteria Tracking methods Computational complexity Tracking accuracy Data association CI Fusion JPDA Medium Medium Simple convex combination fusion MHT High Medium Random finite set GCI/GA fusion PHD Low Low CPHD Medium Medium MeMBer Medium Medium GLMB High High LMB Medium High AA fusion PHD Low Low CPHD Medium Medium MeMBer Medium Medium GLMB High High LMB Medium High
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