臺灣博碩士論文加值系統

隨著科技的發展，雷達多目標追蹤系統不論在軍事國防或民用航空方面，成為廣泛研究的議題。為了因應日益複雜的追蹤環境，提昇追蹤系統之效能極為迫切需要。資料相關結合在多目標追蹤系統中是最重要的技術法則。因為在追蹤多目標物時，資料相關結合可以發現雷達量測值與軌跡間的關聯性；而錯誤的資料結合會導致目標軌跡合成錯誤，無法持續追蹤。
本論文提出一新的追蹤演算法，即應用競爭型類神經網路(Competitive Hopfield Neural Network )之運算架構於雷達目標追蹤系統，此運算將可有效且最佳化地決定雷達感測資料與目標軌跡間的關係，進而準確估算目標物目前的位置及其他資訊。同時應用卡門濾波器估測系統，以獲取最佳估測；系統中加入了適應性程序追蹤架構，更可以成功地解決目標發生變速度(Maneuvering)的問題，進而降低追蹤的誤差及錯誤率。
經模擬驗證結果，本論文提出之競爭型類神經網路追蹤法則能有效應用於多目標追蹤系統並解決資料相關結合與目標追蹤之問題，提昇追蹤系統與資料相關結合的精確性。

As the developing of technology, multiple-target tracking system is an important subject in both national defense and civil application. In order to manage the complicated radar system, enhancing the performance of system is necessary indeed. The main part of the system is data association. While tracking multiple moving targets, data association can find the connection between radar measurement and trajectory. In usual, the wrong data association will lead to the error of target trajectory and cause the loss.
Therefore, in this thesis, a new tracking algorithm Competitive Hopfield Neural Network which is based on the radar target tracking system will efficiently determent the connection of radar measurement and object trajectory and further more to estimate the object position or other related information. By adapting Kalman filter estimation system, CHNN will obtain the great estimate; moreover, with the adaptive procedure tracking technique, the problem of maneuvering will be solved successfully.
As the results of this simulation, this thesis conducts that CHNN can not only apply to the multiple target tracking system but also solves the problem of data association and tracking subjects.

目錄
封面內頁
簽名頁
授權書．．．．．．．．．．．．．．．．．．．．．．．．．iii
中文摘要．．．．．．．．．．．．．．．．．．．．．．．．iv
英文摘要 ．．．．．．．．．．．．．．．．．．．．．．．．v
誌謝．．．．．．．．．．．．．．．．．．．．．．．．．．vi
目錄．．．．．．．．．．．．．．．．．．．．．．．．．．vii
圖目錄 ．．．．．．．．．．．．．．．．．．．．．．．．．x
表目錄．．．．．．．．．．．．．．．．．．．．．．．．．xiii

第一章　緒論
1.1研究動機與目的．．．．．．．．．．．．．．．．1
1.2研究方法與步驟．．．．．．．．．．．．．．2
1.3論文結構．．．．．．．．．．．．．．．．．4
第二章　理論架構
2.1卡門濾波器．．．．．．．．．．．．．．．．．．6
2.2線性卡門濾波器模型．．．．．．．．．．．．．．8
2.3卡門濾波器之數學運算 ．．．．．．．．．．．．10
2.4擴展式卡門濾波器 ．．．．．．．．．．．．．．12
2.5卡門濾波器之相關特性 ．．．．．．．．．．．．14
2.6多目標追蹤程序 ．．．．．．．．．．．．．．．17
2.7類神經網路濾波法 ．．．．．．．．．．．．．．21
2.8雷達系統簡介 ．．．．．．．．．．．．．．．．24
第三章　類神經網路數學架構
3.1前言 ．．．．．．．．．．．．．．．．．．．．32
3.2類神經網路簡介 ．．．．．．．．．．．．．．．33
3.3類神經網路理論 ．．．．．．．．．．．．．．．35
3.4循環網路架構 ．．．．．．．．．．．．．．．．40
3.5 Hopfield類神經網路 ．．．．．．．．．．．．．41
第四章　Data Association 架構
4.1前言 ．．．．．．．．．．．．．．．．．．．．44
4.2 JPDA理論 ．．．．．．．．．．．．．．．．．44
4.31-Step Conditional Maximum Likelihood理論．．．．53
4.4競爭型類神經網路演算法(Competitive Hopfield Neural Network Algorithm)．．．．．．．．．．．．．．55
4.5理論證明 ．．．．．．．．．．．．．．．．．．61
第五章　適應性變速度理論
5.1前言 ．．．．．．．．．．．．．．．．．．．．64
5.2適應性程序 ．．．．．．．．．．．．．．．．．65
5.3多感測資料融合(Multiple Sensor Data Fusion) ．．67
第六章　電腦模擬與分析
6.1前言 ．．．．．．．．．．．．．．．．．．．．70
6.2變速度單一目標追蹤模擬分析 ．．．．．．．．．73
6.3變速度雙目標追蹤模擬分析 ．．．．．．．．．．79
6.4變速度三目標追蹤模擬分析 ．．．．．．．．．．85
6.5變速度四目標追蹤模擬分析 ．．．．．．．．．．91
第七章　結論．．．．．．．．．．．．．．．．．．．．．．98
參考文獻．．．．．．．．．．．．．．．．．．．．．．．．99

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