第1章 绪 论 1
1．1 研究背景 1
1．2 国内外研究现状 2
1．2．1 航迹起始 2
1．2．2 航迹维持 3
1．2．3 机动跟踪 3
1．3 多传感器编队目标跟踪技术中有待解决的一些关键问题 4
1．3．1 杂波环境下编队目标航迹起始技术 4
1．3．2 复杂环境下集中式多传感器编队目标跟踪技术 5
1．3．3 集中式多传感器机动编队目标跟踪技术 5
1．3．4 系统误差下编队目标航迹关联技术 6
1．4 本书的主要内容及安排 7
第2章 编队目标航迹起始算法 8
2．1 引言 8
2．2 基于相对位置矢量的编队目标灰色航迹起始算法 8
2．2．1 基于循环阈值模型的编队预分割 10
2．2．2 基于编队中心点的预互联 11
2．2．3 RPV-FTGTI 算法 12
2．2．4 编队内目标航迹的确认 18
2．2．5 编队目标状态矩阵的建立 19
2．2．6 仿真比较与分析 20
2．2．7 讨论 34
2．3 集中式多传感器编队目标灰色航迹起始算法 35
2．3．1 多传感器编队目标航迹起始框架 35
2．3．2 多传感器预互联编队内杂波的剔除 36
2．3．3 多传感器编队内量测合并模型 37
2．3．4 航迹得分模型的建立 38
2．4 基于运动状态的集中式多传感器编队目标航迹起始算法40
多传感器编队目标跟踪
VIII
2．4．1 同状态航迹子编队获取模型 40
2．4．2 多传感器同状态编队关联模型 45
2．4．3 编队内航迹精确关联合并模型 45
2．5 仿真比较与分析 46
2．5．1 仿真环境 47
2．5．2 仿真结果及分析 47
2．6 本章小结 54
第3章 复杂背景下集中式多传感器编队目标跟踪算法 56
3．1 引言 56
3．2 系统描述 56
3．3 云雨杂波和带状干扰剔除模型 57
3．3．1 云雨杂波剔除模型 58
3．3．2 带状干扰剔除模型 60
3．3．3 验证分析 61
3．4 基于模板匹配的集中式多传感器编队目标跟踪算法 63
3．4．1 基于编队整体的预互联 63
3．4．2 模板匹配模型的建立 65
3．4．3 编队内航迹的状态更新 69
3．4．4 讨论 69
3．5 基于形状方位描述符的集中式多传感器编队目标粒子滤波算法 69
3．5．1 编队目标形状矢量的建立 70
3．5．2 相似度模型的建立 72
3．5．3 冗余图像的剔除 74
3．5．4 基于粒子滤波的状态更新 74
3．6 仿真比较与分析 75
3．6．1 仿真环境 75
3．6．2 仿真结果 76
3．6．3 仿真分析 78
3．7 本章小结 79
第4章 集中式多传感器机动编队目标跟踪算法 81
4．1 引言 81
4．2 典型机动编队目标跟踪模型的建立 82
目 录
IX
4．2．1 编队整体机动跟踪模型的建立 82
4．2．2 编队分裂跟踪模型的建立 85
4．2．3 编队合并跟踪模型的建立 87
4．2．4 编队分散跟踪模型的建立 89
4．3 变结构JPDA机动编队目标跟踪算法 91
4．3．1 事件的定义 92
4．3．2 编队确认矩阵的建立 93
4．3．3 编队互联矩阵的建立 93
4．3．4 编队确认矩阵的拆分 95
4．3．5 概率的计算 97
4．3．6 编队内航迹的状态更新 100
4．4 扩展广义S-维分配机动编队目标跟踪算法 101
4．4．1 基本模型的建立 102
4．4．2 编队量测的划分 103
4．4．3 3-维分配问题的构造 106
4．4．4 广义S-维分配问题的构造 107
4．4．5 编队内航迹的状态更新 107
4．5 仿真比较与分析 108
4．5．1 仿真环境 108
4．5．2 仿真结果 110
4．5．3 仿真分析 113
4．6 本章小结 114
第5章 系统误差下编队目标航迹关联算法 116
5．1 引言 116
5．2 系统误差下基于双重模糊拓扑的编队目标航迹关联算法 116
5．2．1 基于循环阈值模型的编队航迹识别 117
5．2．2 第一重模糊拓扑关联模型 118
5．2．3 第二重模糊拓扑关联模型 123
5．3 系统误差下基于误差补偿的编队目标航迹关联算法 125
5．3．1 编队航迹状态识别模型 125
5．3．2 编队航迹系统误差估计模型 127
5．3．3 误差补偿和编队内航迹的精确关联 130
5．3．4 讨论 130
多传感器编队目标跟踪
X
5．4 仿真比较与分析 131
5．4．1 仿真环境 131
5．4．2 仿真结果及分析 132
5．5 本章小结 134
第6章 结论及展望 135
附录A 式(2-17)中阈值参数ε 的推导 140
附录B 式(5-19)的推导 144
参考文献 148
CONTENTS
Chapter 1 Introduction 1
1．1 Background of Research 1
1．2 Internal and Oversea Research Actualities 2
1．2．1 Track Initiation 2
1．2．2 Track Maintenance 3
1．2．3 Maneuvering Tracking 3
1．3 The Key Problem to Be Resolved in Multi-sensor Formation Targets
Tracking Technique 4
1．3．1 Formation Targets Track Initiation Technique with Clutter 4
1．3．2 Centralized Multi-sensor Formation Targets Tracking Technique
with the Complicated Background 5
1．3．3 Centralized Multi-sensor Maneuvering Formation Targets Tracking
Technique 5
1．3．4 Track Correlation Technique of the Formation Targets with
Systematic Errors 6
1．4 Main Content and Arragement of Dissertation 7
Chapter 2 Formation Targets Track Initiation Algorithm 8
2．1 Introduction 8
2．2 Formation Targets Gray Track Initiation Algorithm Based on Relative
Position Vector 8
2．2．1 Preparative Division of the Formation Targets Based on the
Circulatory Threshold Model 10
2．2．2 Preparative Association Based on the Formation Center 11
2．2．3 RPV-FTGTI Algorithm 12
2．2．4 Validation of the Tracks in the Formation 18
2．2．5 Establishment of the Formation Target State Matrix 19
2．2．6 Simulation Comparision and Analysis 20
2．2．7 Discussion 34
2．3 Centralized Multi-sensor Formation Targets Gray Track Initiation
Algorithm 35
2．3．1 Multi-sensor Formation Targets Track Initiation Frame 35
2．3．2 Multi-sensor Clutter Deletion in Preparative Associated
多传感器编队目标跟踪
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Formations 36
2．3．3 Multi-sensor Measurement Mergence Model in the Formation 37
2．3．4 Establishment of the Track Score Model 38
2．4 Centralized Multi-sensor Formation Targets Track Initiation Algorithm
Based on Moving State 40
2．4．1 Same-state Track SubFormation Obtainment Model 40
2．4．2 Multi-sensor Same-state Formation Association Model 45
2．4．3 Accurate Association and Mergence Model of the Formation
Tracks 45
2．5 Simulation Comparision and Analysis 46
2．5．1 Simulation Envirenment 47
2．5．2 Simulation Results and Analysis 47
2．6 Summary 54
Chapter 3 Centralized Multi-sensor Formation Targets Tracking Algorithm with the
Complicated Background 56
3．1 Introduction 56
3．2 System Description 56
3．3 Deletion Models of the Cloud-rain Clutter and the Narrow-Band
Interference 57
3．3．1 Cloud-rain Clutter Deletion Model 58
3．3．2 Narrow-Band Interference Deletion Model 60
3．3．3 Validation and Analysis 61
3．4 Centralized Multi-sensor Formation Targets Tracking Algorithm Based on
Template Matching 63
3．4．1 Preparative Association Based on the Whole Formation 63
3．4．2 Establishment of the Template Matching Model 65
3．4．3 State Update of the Tracks in the Formation 69
3．4．4 Discussion 69
3．5 Centralized Multi-sensor Formation Targets Particle Filter Based on Shape
and Azimuth Descriptor 69
3．5．1 Establishment of the Formation Targets Shape Vector 70
3．5．2 Establishment of the Resemble Model 72
3．5．3 Deletion of the Redundant Picture 74
3．5．4 State Update Based on Particle Filter 74
CONTENTS
XIII
3．6 Simulation Comparision and Analysis 75
3．6．1 Simulation Envirenment 75
3．6．2 Simulation Results 76
3．6．3 Simulation Analysis 78
3．7 Summary 79
Chapter 4 Centralized Multi-sensor Maneuvering Formation Targets Tracking
Algorithm 81
4．1 Introduction 81
4．2 Establishment of Typical Maneuvering Formation Targets Tracking
Models 82
4．2．1 Establishment of the Formation Whole Maneuver Tracking
Model 82
4．2．2 Establishment of the Formation Splitting Tracking Model 85
4．2．3 Establishment of the Formation merging Tracking Model 87
4．2．4 Establishment of the Formation dispersing Tracking Model 89
4．3 Maneuvering Formation Targets Tracking Algorithm Based on Different
Structure JPDA Technique 91
4．3．1 Event Definition 92
4．3．2 Establishment of the Formation Validation Matrix 93
4．3．3 Establishment of the Formation Association Matrix 93
4．3．4 Splitting of the Formation Validation Matrix 95
4．3．5 Calculation of the Probability 97
4．3．6 State Update of the Tracks in the Formation 100
4．4 Maneuvering Formation Targets Tracking Algorithm Based on Patulous
Generalized S-D Assignment Technique 101
4．4．1 Establishment of the Basic Model 102
4．4．2 Partition of the Measurements of the Formation Targets 103
4．4．3 Conformation of 3-D Assignment Problem 106
4．4．4 Conformation of Generalized S-D Assignment Problem 107
4．4．5 State Update of the Tracks in the Formation 107
4．5 Simulation Comparision and Analysis 108
4．5．1 Simulation Envirenment 108
4．5．2 Simulation Results 110
4．5．3 Simulation Analysis 113
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4．6 Summary 114
Chapter 5 Formation Targets Track Correlation Algorithm with Systematic
Errors 116
5．1 Introduction 116
5．2 Formation Targets Track Correlation Algorithm with Systematic Errors
Based on Double Fussy Topology 116
5．2．1 Formation Tracks Identification Based on Circulatory Threshold
Model 117
5．2．2 The First Scale Fussy Topology Model 118
5．2．3 The Second Scale Fussy Topology Model 123
5．3 Formation Targets Track Correlation Algorithm with Systematic Errors
Based on Error Compensation 125
5．3．1 Formation Track State Identification Model 125
5．3．2 Formation Track Systematic Error Estimation Model 127
5．3．3 Error Compensation and Formation Track Accurate
Correlation 130
5．3．4 Discussion 130
5．4 Simulation Comparision and Analysis 131
5．4．1 Simulation Envirenment 131
5．4．2 Simulation Results and Analysis 132
5．5 Summary 134
Chapter 6 Conclusions and Prospects 135
Appendix A Illation of the Threshold Parameter ε in Formula (2-17) 140
Appendix B Illation of Formula (5-19) 144
References 148
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