當超寬頻系統(Ultra-Wideband radio)於室內密集多重路徑通道環境中，進行行動台定位與路徑追蹤時，針對非直視(non -light of sight, NLOS)傳播產生之誤差，本篇論文提出使用含有抵達時間差定位法(Time Difference of Arrival, TDOA)原理之偏移擴展式卡爾曼濾波器(biased extended Kalman filter, BEKF) 加以抑制。在以時間定位之系統中，非直視誤差是影響定位準確率最大的問題，因此利用卡爾曼濾波器來平滑距離量測曲線，並提出運用滑動視窗的方法處理距離數據，藉由計算標準差後進行假設檢定，能有效且即時地鑑別出非直視傳播，再由論文中提出根據基地台之間不同傳播的狀態，將量測到之抵達時間轉換為距離差後，使用具有抑制非直視誤差機制之偏移擴展式卡爾曼濾波器，估測出行動台位置且追蹤其移動軌跡。最後於存在量測及非直視誤差之室內定位環境下進行模擬，由模擬結果證實，本論文提出之滑動視窗演算法與偏移擴展式卡爾曼濾波器，和相關非直視傳播鑑別及抑制方法比較效能，可以更有效地得到行動台位置。

This thesis presents mobile positioning and tracking with non-line of sight (NLOS) mitigation using time difference of arrival (TDOA) in biased extended Kalman filter (BEKF) in indoor dense multipath Ultra-Wideband (UWB) environment. The most serious issues which render to influence accuracy for the time-based location system is NLOS problem. Kalman filters (KFs) are used for smoothing range measurement data, and a method with sliding window is proposed to process range data for calculating standard deviation in a hypothesis testing and then identifying NLOS scenarios. When the measured arrival time has been converted to range difference, the biased extended Kalman filter is proposed to mitigate the NLOS error in the certain base stations (BSs) for mobile station (MS) positioning and trajectory tracking. From the simulation results in the indoor positioning environment with measurement and NLOS error, the sliding window algorithm and biased extended Kalman filter have higher accuracy than other related methods for NLOS identification and mitigation in positioning.

誌謝………………………………………………………………i
摘要………………………………………………………………ii
Abstract…………………………………………………………iii
目錄………………………………………………………………iv
圖目錄……………………………………………………………vi
表目錄……………………………………………………………viii
第一章 緒論………………………………………………………1
1.1研究背景………………………………………………………2
1.2研究動機………………………………………………………3
1.3論文架構………………………………………………………4
第二章 無線定位法與距離模型…………………………………5
2.1時間應用定位法………………………………………………5
2.1.1抵達時間定位法……………………………………………7
2.1.2抵達時間差定位法…………………………………………9
2.2距離量測模型…………………………………………………12
2.3超寬頻系統非直視誤差模型…………………………………12
第三章 非直視傳播之鑑別與抑制方法…………………………16
3.1數據曲線平滑方法……………………………………………16
3.1.1多項式擬合法………………………………………………16
3.1.2卡爾曼濾波器………………………………………………17
3.2鑑別非直視傳播演算法………………………………………25
3.2.1區間查驗法…………………………………………………25
3.2.2滑動視窗法…………………………………………………28
3.3抑制非直視誤差方法…………………………………………30
3.3.1直視距離重建法……………………………………………30
3.3.2偏移卡爾曼濾波器…………………………………………32
3.3.3偏移擴展式卡爾曼濾波器…………………………………34
第四章 室內定位模擬及分析……………………………………45
4.1數據曲線平滑方法……………………………………………45
4.1.1模擬環境……………………………………………………45
4.2非直視傳播之鑑別……………………………………………47
4.2.1模擬結果與討論分析………………………………………47
4.3非直視傳播環境下行動台之定位……………………………55
4.3.1模擬結果與討論分析………………………………………56
第五章 結論與未來方向…………………………………………65
5.1結論……………………………………………………………65
5.2未來工作與展望………………………………………………66
參考文獻 …………………………………………………………67

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