臺灣博碩士論文加值系統

在現今的航空及國防科技，由於目標物的性能速度，數目及變異性等皆較以往複雜，為因應日益復雜的追蹤環境，追蹤系統的性能必須同步提昇，才能達到洞燭機先，決戰千里之外的最高戰術目的。在目標的追蹤上，追蹤多個目標時比較複雜，也會常常造成感測器判斷上的錯誤，或者造成追蹤上極大的誤差，本論文提出結合影像資料及數據資料之相關結合技術，即應用競爭性類神經網路(Competitive Hopfield Neural Network)運算法於追蹤系統，擷取其特殊的運算架構，將之應用於本研究，並結合適應性預估器追蹤架構，以達到最佳的追蹤效果。當感測器偵測到訊號之後，經過影像處理程序與資料結合相關技術，提供目標運動的資訊供追蹤系統參考判斷用，進而判別出正確的雷達感測資料與目標軌跡關係。相信應用本論文所提之追蹤架構，將可得到較佳的追蹤結果。

Multiple-target tracking (MTT) is a prerequisite step for radar surveillance systems. Data association is the key technique in a radar multiple-target tracking system. A new approach to data association using both quantity data and image information is investigated in this dissertation. In order to combine two different attributes, a fusion algorithm based on the Competitive Hopfield Neural Network (CHNN) is developed to match between radar measurements and existing target tracks. When target maneuvering problems are occurred, an adaptive maneuvering estimator is applied. Based on the computation algorithm, we convince that this approach can successfully solve the multiple-target tracking problems and have better performance.

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..........ix
表目錄...........x

第一章　緒論..........1
1.1 研究動機......1
1.2 研究方法.......3
1.3 論文結構........4

第二章　卡門濾波器原理.......5
2.1 前言........5
2.2 卡門濾波器......5
2.3 卡門濾波器之性質...9

第三章　資料相關結合理論.........11
3.1 前言........11
3.2 追蹤掃瞄區域預測技術........11
3.3 影像辨認流程............15
3.4 資料相關結合技術.......21

第四章　適應性預估器........26
4.1 前言.........26
4.2 變速度追蹤理論.....26

第五章　模擬結果分析........29
5.1 前言........29
5.2 影像模擬結果.........29
5.3 模擬結果及分析........31

第六章　結論.......39

參考文獻.........40

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