本論文的室內定位系統(Indoor Positioning System, IPS)採用超寬頻(Ultra-WideBand, UWB)通訊技術開發，再將各個固定端(Anchor)之測距值代入三角定位演算法(Triangulation)中計算出三角定位點，以得出即時定位點之絕對位置座標，但是系統必定會因為環境因素及系統所擺佈的網路拓樸架構差異造成之定位點飄動誤差，所以本論文主要探討即時定位點在不同環境條件下之定位點飄動情形，並進行定位點飄動誤差的分析與改善，定位點飄動誤差分析方式分別使用移動平均(Moving Average, MA )演算法、最小平方法(Least Square, LS)、卡爾曼濾波(Kalman Filter, KF)演算法及本論文提出的改良型自適應卡爾曼濾波(Improved Adaptive Kalman Filter, IAKF)演算法，將實際量測之定位點座標值分別代入以上提出的四種演算法中運算並比較各個演算法在定位點飄動誤差上改善幅度的大小，最後將本論文提出的改良型自適應卡爾曼濾波演算法在超寬頻室內定位系統的硬體上實現與驗證，以改善即時定位點飄動誤差，使即時定位點之量測位置座標更接近實際位置座標之被定位目標點(Tag)。

The indoor positioning system (IPS) uses ultra-wideband (UWB) communication technology in the thesis. The positioning method employs the triangulation positioning algorithms to calculate the position of a target. However, the system must have shifting errors due to environmental factors and different network topologies. Therefore, the thesis mainly discusses the shifting error of the real-time positioning point under different environmental factors. The thesis will show the improvement of the shift error of the positioning point. The improvement algorithm uses moving average (MA) method, least square (LS) method, kalman filter (KF) and the improved adaptive kalman filter (IAKF) algorithm proposed in the thesis. All the measured coordinates of the positioning points are put into the above four algorithms for improvement. The improvement magnitude of each algorithm is compared in the shift error of the positioning point. Finally, the thesis proposes an IAKF algorithm, which is implemented and verified on the UWB indoor positioning system. The shift error of the positioning point is improved, so that the coordinates of the real-time positioning point are closer to the target point of the actual position.

摘要......................i
Abstract......................ii
誌謝......................iii
目錄......................iv
表目錄......................vi
圖目錄......................vii
第一章 緒論......................1
1.1 背景......................1
1.2 研究動機......................2
1.3 系統架構......................3
1.4 論文章節架構......................4
第二章 相關背景知識介紹......................5
2.1 室內定位系統介紹......................5
2.2 超寬頻室內定位系統介紹......................14
2.2.1 超寬頻無線通訊技術與通道頻段分配介紹......................14
2.2.2 超寬頻室內定位系統協定架構與動作流程介紹......................17
2.3 雙向測距推算法......................22
2.4 三角定位法......................23
第三章 超寬頻室內定位系統之定位點飄動分析......................25
3.1 距離計算原理......................25
3.1.1 接收訊號到達時間計算方法......................25
3.1.2 接收訊號到達時間差計算方法......................28
3.1.3 接收訊號角度計算方法......................30
3.1.4 接收訊號強度指標計算方法......................32
3.2 定位點飄動改善演算法介紹......................34
3.2.1 移動平均法......................34
3.2.2 最小平方法......................36
3.2.3 卡爾曼濾波演算法......................38
3.2.4 改良型自適應卡爾曼濾波演算法......................41
3.3 演算法模擬結果分析......................43
3.3.1 移動平均法之定位點飄動誤差改善......................44
3.3.2 最小平方法之定位點飄動誤差改善......................48
3.3.3 卡爾曼濾波演算法之定位點飄動誤差改善......................52
3.3.4 改良型自適應卡爾曼濾波演算法之定位點飄動誤差改善......................56
3.3.5 各個演算法之定位點飄動誤差改善比較......................60
第四章 實驗結果......................68
4.1 靜態定位點飄動誤差量測與改善後量測結果......................68
4.2 人為干擾之定位點飄動誤差量測與改善後量測結果......................72
4.3 動態定位點飄動誤差量測與改善後量測結果......................77
第五章 結論......................81
參考文獻......................82
Extended Abstract......................86

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