超寬頻(Ultra-Wideband, UWB)訊號具高度的距離解析能力，在無線感測器網路可提供精確的定位服務。藉由多感測器來追蹤目標可提供較佳的效能，但集中式演算法卻不適用於無線感測器網路。此外，非視線(Non Light of Sight, NLOS)傳播誤差導致定位系統之精確度嚴重下降。在本篇論文中，提出了非視線鑑別與抑制之技術，利用修改的偏移式卡爾曼濾波器來降低超寬頻環境中非視線傳播的抵達時間(Time of Arrival, TOA)誤差。我們結合修改後的偏移式卡爾曼濾波器與滑動視窗針對不同程度的非視線誤差進行立即地鑑別與抑制。
為了處理不精確的非視線抵達角度之影響，我們也針對抵達角度(Angle of Arrival, AOA)的選擇與融合方法進行探討。在分佈式定位架構方面，我們使用了擴展式資訊濾波器(Extended Information Filter, EIF)來處理抵達時間差(Time Difference of Arrival, TDOA)與抵達角度之測量值，並有效進行目標定位與追蹤。以擴展式資訊濾波器取代擴展式卡爾曼濾波器(Extended Kalman Filter, EKF)可容易地吸收選擇的抵達角度而不會造成維度動態的變化。為了減輕運算量，感測器被分為不同的群組完成分佈式的抵達時間差與抵達角度定位，然後各群組可容易地消化其他群組的資訊來維持精確的定位。
模擬結果驗證所提出之架構能有效抑制非視線誤差，並於無線感測器網路中，透過分佈式定位與資料融合提昇位置估測與目標追蹤之精確度。

Ultra Wideband (UWB) signal can offer an accurate location service in wireless sensor networks because its high range resolution. Target tracking by multiple sensors can provide better performance, but the centralized algorithms are not suitable for wireless sensor networks. In additional, the non line of sight (NLOS) propagation error leads to severe degradation of the accuracy in location systems. In this thesis, NLOS identification and mitigation technique utilizing modified biased Kalman filter (KF) is proposed to reduce the NLOS time of arrival (TOA) errors in UWB environments. We combine the modified biased Kalman filter with sliding window to identify and mitigate different degree of NLOS errors immediately.
In order to deal with the influence of inaccurate NLOS angle of arrival (AOA) measurements, we also had a discussion on AOA selection and fusion methods. In the distributed location structure, we used the extended Information filter (EIF) to process the formulated time difference of arrival (TDOA) and AOA measurements for the target positioning and tracking. Instead of using extended Kalman filter, extended Information filter can assimilate selected AOA easily without dynamic dimensions. The sensors are divided into different groups for distributed TDOA/AOA location to reduce computation and then each group can assimilate information from other groups easily to maintain precise location.
The simulation results show that the proposed architecture can mitigate NLOS errors effectively and improve the accuracy of target positioning and tracking from distributed location and data fusion in wireless sensor networks.

誌謝.............................................................................................................i
摘要............................................................................................................ii
Abstract..................................................................................................... iii
目錄...........................................................................................................iv
圖目錄.......................................................................................................vi
表目錄........................................................................................................x
第一章 緒論..............................................................................................1
1.1 研究背景.....................................................................................1
1.2 研究動機.....................................................................................2
1.3 論文架構.....................................................................................3
第二章 無線定位法與距離模型..............................................................4
2.1 無線定位原理.............................................................................4
2.1.1 接收訊號強度定位法......................................................4
2.1.2 訊號抵達角度定位法......................................................5
2.1.3 訊號抵達時間定位法......................................................6
2.1.4 訊號抵達時間差定位法..................................................8
2.1.5 混合式定位法................................................................10
2.2 距離量測模型...........................................................................11
2.3 超寬頻系統非視線誤差模型...................................................12
第三章 非視線傳播之鑑別與抑制方法................................................14
3.1 非視線傳播之鑑別方法...........................................................14
3.1.1 多項式擬合法................................................................15
3.1.2 卡爾曼濾波器................................................................16
3.2 非視線誤差之抑制方法...........................................................21
3.2.1 直視距離重建法............................................................22
3.2.2 偏移式卡爾曼濾波器....................................................23
3.3 非視線傳播鑑別與抑制方法之結合.......................................30
3.4 抵達角度之選擇與探討...........................................................38
第四章 具抑制非視線誤差之TDOA/AOA 定位法.............................39
4.1 擴展式卡爾曼濾波器...............................................................39
4.2 多感測器資料融合...................................................................41
4.2.1 資訊濾波器....................................................................42
4.2.2 解中心化之計算方法....................................................44
4.3 使用擴展式資訊濾波器之分佈式定位...................................45
4.3.1 分佈式定位系統架構....................................................45
4.3.2 擴展式資訊濾波器........................................................46
4.3.3 結合AOA 選擇之分佈式TDOA 定位........................47
4.4 資訊矩陣與幾何精度稀釋.......................................................50
第五章 電腦模擬與分析........................................................................55
5.1 具非視線誤差抑制之定位模擬...............................................55
5.1.1 抑制非視線抵達時間誤差之模擬................................57
5.1.2 抵達角度選擇與定位效能之模擬................................67
5.2 非視線情形下分佈式定位與融合之模擬...............................69
5.2.1 感測器數量對定位效能之影響....................................70
5.2.2 非視線感測器數量對融合定位效能之影響................72
5.2.3 感測器分組方式對融合定位效能之影響....................81
第六章 結論與建議................................................................................85
參考文獻..................................................................................................87

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