多傳感器編隊(duì)目標(biāo)跟蹤技術(shù)

第1章 緒 論······································································································ 1 1.1 研究背景········································································································· 1 1.2 國內(nèi)外研究現(xiàn)狀····························································································· 2 1.2.1 航跡起始····························································································· 2 1.2.2 航跡維持····························································································· 3 1.2.3 機(jī)動跟蹤····························································································· 3 1.3 多傳感器編隊(duì)目標(biāo)跟蹤技術(shù)中有待解決的一些關(guān)鍵問題························· 4 1.3.1 雜波環(huán)境下編隊(duì)目標(biāo)航跡起始技術(shù)················································ 4 1.3.2 復(fù)雜環(huán)境下集中式多傳感器編隊(duì)目標(biāo)跟蹤技術(shù)···························· 5 1.3.3 集中式多傳感器機(jī)動編隊(duì)目標(biāo)跟蹤技術(shù)········································ 5 1.3.4 系統(tǒng)誤差下編隊(duì)目標(biāo)航跡關(guān)聯(lián)技術(shù)················································ 6 1.4 本書的主要內(nèi)容及安排················································································· 7 第2章 編隊(duì)目標(biāo)航跡起始算法·········································································· 8 2.1 引言················································································································· 8 2.2 基于相對位置矢量的編隊(duì)目標(biāo)灰色航跡起始算法····································· 8 2.2.1 基于循環(huán)閾值模型的編隊(duì)預(yù)分割·················································· 10 2.2.2 基于編隊(duì)中心點(diǎn)的預(yù)互聯(lián)······························································ 11 2.2.3 RPV-FTGTI 算法············································································· 12 2.2.4 編隊(duì)內(nèi)目標(biāo)航跡的確認(rèn)·································································· 18 2.2.5 編隊(duì)目標(biāo)狀態(tài)矩陣的建立······························································ 19 2.2.6 仿真比較與分析·············································································· 20 2.2.7 討論··································································································· 34 2.3 集中式多傳感器編隊(duì)目標(biāo)灰色航跡起始算法················································ 35 2.3.1 多傳感器編隊(duì)目標(biāo)航跡起始框架·················································· 35 2.3.2 多傳感器預(yù)互聯(lián)編隊(duì)內(nèi)雜波的剔除·············································· 36 2.3.3 多傳感器編隊(duì)內(nèi)量測合并模型······················································ 37 2.3.4 航跡得分模型的建立······································································ 38 2.4 基于運(yùn)動狀態(tài)的集中式多傳感器編隊(duì)目標(biāo)航跡起始算法························40 多傳感器編隊(duì)目標(biāo)跟蹤 ·VIII· 2.4.1 同狀態(tài)航跡子編隊(duì)獲取模型·························································· 40 2.4.2 多傳感器同狀態(tài)編隊(duì)關(guān)聯(lián)模型······················································ 45 2.4.3 編隊(duì)內(nèi)航跡精確關(guān)聯(lián)合并模型······················································ 45 2.5 仿真比較與分析··························································································· 46 2.5.1 仿真環(huán)境··························································································· 47 2.5.2 仿真結(jié)果及分析·············································································· 47 2.6 本章小結(jié)······································································································· 54 第3章 復(fù)雜背景下集中式多傳感器編隊(duì)目標(biāo)跟蹤算法································· 56 3.1 引言··············································································································· 56 3.2 系統(tǒng)描述······································································································· 56 3.3 云雨雜波和帶狀干擾剔除模型··································································· 57 3.3.1 云雨雜波剔除模型·········································································· 58 3.3.2 帶狀干擾剔除模型·········································································· 60 3.3.3 驗(yàn)證分析··························································································· 61 3.4 基于模板匹配的集中式多傳感器編隊(duì)目標(biāo)跟蹤算法······························· 63 3.4.1 基于編隊(duì)整體的預(yù)互聯(lián)·································································· 63 3.4.2 模板匹配模型的建立······································································ 65 3.4.3 編隊(duì)內(nèi)航跡的狀態(tài)更新·································································· 69 3.4.4 討論··································································································· 69 3.5 基于形狀方位描述符的集中式多傳感器編隊(duì)目標(biāo)粒子濾波算法··········· 69 3.5.1 編隊(duì)目標(biāo)形狀矢量的建立······························································ 70 3.5.2 相似度模型的建立·········································································· 72 3.5.3 冗余圖像的剔除·············································································· 74 3.5.4 基于粒子濾波的狀態(tài)更新······························································ 74 3.6 仿真比較與分析··························································································· 75 3.6.1 仿真環(huán)境··························································································· 75 3.6.2 仿真結(jié)果··························································································· 76 3.6.3 仿真分析··························································································· 78 3.7 本章小結(jié)······································································································· 79 第4章 集中式多傳感器機(jī)動編隊(duì)目標(biāo)跟蹤算法············································· 81 4.1 引言··············································································································· 81 4.2 典型機(jī)動編隊(duì)目標(biāo)跟蹤模型的建立··························································· 82 目 錄 ·IX· 4.2.1 編隊(duì)整體機(jī)動跟蹤模型的建立······················································ 82 4.2.2 編隊(duì)分裂跟蹤模型的建立······························································ 85 4.2.3 編隊(duì)合并跟蹤模型的建立······························································ 87 4.2.4 編隊(duì)分散跟蹤模型的建立······························································ 89 4.3 變結(jié)構(gòu)JPDA機(jī)動編隊(duì)目標(biāo)跟蹤算法······················································· 91 4.3.1 事件的定義······················································································· 92 4.3.2 編隊(duì)確認(rèn)矩陣的建立······································································ 93 4.3.3 編隊(duì)互聯(lián)矩陣的建立······································································ 93 4.3.4 編隊(duì)確認(rèn)矩陣的拆分······································································ 95 4.3.5 概率的計(jì)算······················································································· 97 4.3.6 編隊(duì)內(nèi)航跡的狀態(tài)更新································································ 100 4.4 擴(kuò)展廣義S-維分配機(jī)動編隊(duì)目標(biāo)跟蹤算法············································ 101 4.4.1 基本模型的建立············································································ 102 4.4.2 編隊(duì)量測的劃分············································································ 103 4.4.3 3-維分配問題的構(gòu)造····································································· 106 4.4.4 廣義S-維分配問題的構(gòu)造···························································· 107 4.4.5 編隊(duì)內(nèi)航跡的狀態(tài)更新································································ 107 4.5 仿真比較與分析························································································· 108 4.5.1 仿真環(huán)境························································································· 108 4.5.2 仿真結(jié)果························································································· 110 4.5.3 仿真分析························································································· 113 4.6 本章小結(jié)····································································································· 114 第5章 系統(tǒng)誤差下編隊(duì)目標(biāo)航跡關(guān)聯(lián)算法·················································· 116 5.1 引言············································································································· 116 5.2 系統(tǒng)誤差下基于雙重模糊拓?fù)涞木庩?duì)目標(biāo)航跡關(guān)聯(lián)算法····················· 116 5.2.1 基于循環(huán)閾值模型的編隊(duì)航跡識別············································ 117 5.2.2 第一重模糊拓?fù)潢P(guān)聯(lián)模型···························································· 118 5.2.3 第二重模糊拓?fù)潢P(guān)聯(lián)模型···························································· 123 5.3 系統(tǒng)誤差下基于誤差補(bǔ)償?shù)木庩?duì)目標(biāo)航跡關(guān)聯(lián)算法····························· 125 5.3.1 編隊(duì)航跡狀態(tài)識別模型································································ 125 5.3.2 編隊(duì)航跡系統(tǒng)誤差估計(jì)模型························································ 127 5.3.3 誤差補(bǔ)償和編隊(duì)內(nèi)航跡的精確關(guān)聯(lián)············································ 130 5.3.4 討論································································································· 130 多傳感器編隊(duì)目標(biāo)跟蹤 ·X· 5.4 仿真比較與分析························································································· 131 5.4.1 仿真環(huán)境························································································· 131 5.4.2 仿真結(jié)果及分析············································································ 132 5.5 本章小結(jié)····································································································· 134 第6章 結(jié)論及展望·························································································· 135 附錄A 式(2-17)中閾值參數(shù)ε 的推導(dǎo)··························································· 140 附錄B 式(5-19)的推導(dǎo)····················································································· 144 參考文獻(xiàn)·············································································································· 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 多傳感器編隊(duì)目標(biāo)跟蹤 ·XII· 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 多傳感器編隊(duì)目標(biāo)跟蹤 ·XIV· 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