自20世紀80年代以來，在大城市的交通堵塞已經給人們的出行帶來極大的不便。依據傳統經驗進行交通管理已不能解決交通困難的問題，於是各先進國家為了解決共同面臨的交通問題，競相投入大量資金和人力，開始大規模地進行道路交通運輸智慧化的研究試驗。在智慧型交通運輸系統當中，載具導航為其中重要的一個環節，而要實現導航的關鍵在於具有準確的載具定位估計，目前大部份的導航系統採用全球定位系統（Global Positioning System，以下簡稱GPS）輔助載具的導航系統導航。然而GPS在某些情況下，例如由於衛星之幾何關係引起訊號接收不良、室內導航無法接收衛星訊號、甚至於GPS本身發生故障，以上原因都有可能造成載具定位誤差增加，甚至於無法定位而導致導航失效。基於這些原因，本研究提出一個被動式的定位方法，即考量在無GPS的情況下，載具利用分散式被動感測器網路，其中包含多基地台(輻射電磁波或紅外線且位置已知)，以及載具本身裝置有方位角度感測器，接收來自於多基地台的訊號，利用僅有角度的量測數據及多方來源資訊融合技術：混合座標系濾波技術、階層式估計融合演算法，進行載具動態定位。本研究利用兩座基地台與載具形成分散式被動感測器網路，以及利用預先規劃的路徑與載具的位置估計進行比對，運用蒙地卡羅電腦模擬技術，重複實驗，以驗證本方法之有效性。

Since the 80s of the 20th century, the traffic jams during the morning and night rush in the big cities have brought many inconveniences to the residents. Relying on conventional experiences to manage traffic can not resolve the problem of traffic difficulties. In order to resolve the traffic difficulties, many advance countries invested enormous amount of funds and manpower and began the large-scaled researches to intellectualize road transportation. In the intelligent transportation system, the vehicle navigation is one of the most important issues. The key to accomplish navigation relies on the accuracy of the vehicle location estimation. Conventional vehicle location requires the use of Global Positioning System (GPS) in assisting the vehicle navigation system; however, under some certain circumstances, the GPS may have problems due to the geographical locations of the satellites, the signal obstruction such as indoor navigation and even the malfunctioning of the GPS itself. The present research proposes a concept under consideration of situation where GPS is not available. The vehicle only utilizes distributed passive sensor network including multiple base stations (landmarks) and a direction finder installed on the vehicle for navigation location. The on-board sensor receives angular signals from the landmarks, and then the nonlinear estimation fusion algorithm based on a group of hybrid coordinate (HC) filters is proposed to estimate both position and velocity of the vehicle for autonomous navigation. Monte Carlo simulation using two landmarks is adopted to evaluate the performance of the proposed algorithm.

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誌謝
摘 要
Abstract
目 錄
圖目錄
表目錄
第一章 緒論
1.1 研究背景與動機
1.2 研究目的
1.3 研究限制與流程
第二章 文獻探討
2.1感測器網路與資訊融合
2.1.1感測器網路
2.1.2分散式資訊融合演算法
2.2 通訊定位原理
2.2.1 訊號抵達時間差
2.2.2 訊號抵達時間
2.2.3 訊號強度
2.2.4 混合電波訊號抵達時間差與角度
2.2.5 訊號抵達角度
2.3擴展型卡爾曼濾波演算法與資訊融合演算法
2.3.1修正型球面座標系擴展型卡爾曼濾器與資訊融合演算法
2.3.2混合座標系卡爾曼濾器與資訊融合演算法
第三章 研究步驟與方法
3.1研究步驟
3.2電腦模擬流程圖
第四章 模擬結果與分析
4.1 模擬初始值設定與效能指標
4.2 階梯形軌跡
4.2.1場景說明
4.2.2模擬結果與小結
4.3 跑馬場型
4.3.1場景說明
4.3.2模擬結果與小結
4.4 綜合分析
第五章 結論與未來研究方向
參考文獻

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