在定位演算法中，影響定位精準度的因素有非視線傳播 (Non-Line of Sight, NLOS) 與多重路徑干擾等。因此本論文提出針對非視線傳播誤差進行抑制之演算法，首先利用基於訊號抵達時間 (Time of Arrival, TOA) 量測值之改良型偏移式卡爾曼濾波器 (Improved Biased Kalman filter, IBKF) 進行非視線傳播誤差之鑑別與抑制，藉由非視線傳播誤差成份之訊號標準差遠大於一般量測標準差的特性，結合假設檢定法與滑動視窗檢查法達到即時之非視線傳播誤差鑑別。在獲得目前所處之視線狀態後，回授標準差計算值與鑑別結果來切換偏移/非偏移 (biased/unbiased) 卡爾曼濾波器進行估測，但估測值依然會受到非視線傳播誤差的些微影響。另一方面，基於接收訊號強度 (Received Signal Strength, RSS) 量測值之擴展式卡爾曼濾波器 (Extended Kalman Filter, EKF) 是針對事先已知所處環境模型而設計之，因此非視線傳播誤差之抑制效果會比較顯著。然而即使 EKF-RSS 擁有較佳之抑制效果，卻在非視線傳播誤差之鑑別部分有錯誤率過高的缺點存在。此外， EKF-RSS 在初始即為非視線傳播狀態時，必須花費較長的一段時間才能夠使其估測值達到收斂。經過兩者之分析比較，本論文利用 IBKF-TOA 與 EKF-RSS 能夠互補其缺點的特性，提出將 IBKF-TOA 與 EKF-RSS 應用於交互式多模演算法 (Interacting Multiple Model, IMM) 中的濾波器模組，並且有別於傳統之 IMM 架構，而在 IMM 系統中加入非視線傳播誤差之鑑別部分，藉由 IBKF-TOA 之良好鑑別結果與交互式多模演算法中的軟性切換，達到良好的整體輸出結果。論文中以超寬頻 (Ultra-Wideband, UWB) 訊號環境進行模擬，探討當無線定位系統中的基地台遭受到非視線傳播誤差影響時，對於本論文提出基於交互式多模演算法之資料融合架構、 IBKF-TOA 與 EKF-RSS 三者進行比較。由模擬結果可以得知，本論文所提出之架構能較有效的抑制非視線傳播誤差，並且透過資料融合提升定位估測與目標物追蹤之精準度。

The major problems encountered in wireless location are the effects caused by non-line of sight (NLOS) propagation and multipath interference. In the thesis, we propose an approach to mitigate NLOS error. First of all, we use improved biased Kalman filter (IBKF) based on time of arrival (TOA) measurement to identify and mitigate NLOS error. Applying the statistic characteristic that the standard deviation of the NLOS propagation errors is generally much larger than that of measurement noises in the LOS condition, we combine hypothesis test and sliding window to identify NLOS error. According to the feedback identification and the calculated standard deviation, IBKF switches biased or unbiased to process TOA measurement. Nevertheless, the performance of IBKF-TOA is still affected slightly by NLOS error. Since extended Kalman filter (EKF) based on received signal strength (RSS) measurement is designed for prespecified environments, the effect of NLOS mitigation is more obvious. Moreover, EKF-RSS not only exists higher error probability in NLOS identification, but also needs longer time to converge in the cases that start with NLOS. Comparing IBKF-TOA with EKF-RSS, we adopt interacting multiple model (IMM) in the proposed data fusion structure for processing TOA and RSS measurements. In the proposed scheme, we reserve the basic IMM structure and add the step of NLOS identification into basic IMM structure. By accurate NLOS identification results and soft decision of IMM, the proposed scheme will switch to adequate filter mode and obtain better estimation. With simulation in UWB channel, the analysis and performance evaluation show advantages and disadvantages of using IBKF-TOA, EKF-RSS, and proposed scheme. Simulation results reveal that NLOS error can be mitigated effectively by data fusion of TOA and RSS measurements and can achieve high accuracy in positioning and tracking.

誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
英文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
圖目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
表目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
1 緒論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 研究背景. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 研究動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 非視線傳播誤差之鑑別與抑制. . . . . . . . . . . . . . . . . . 4
2.1 量測模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.1 距離量測模型. . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.2 訊號衰減模型. . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 非視線傳播誤差之鑑別. . . . . . . . . . . . . . . . . . . . . 5
2.2.1 使用卡爾曼濾波器之資料平滑. . . . . . . . . . 6
2.2.2 標準差假設檢定法結合滑動視窗之非視線傳
播誤差鑑別. . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 非視線傳播誤差之抑制. . .. . . . . . . . . . . . . . . . . . 10
2.3.1 偏移式卡爾曼濾波器. . . . . . . . . . . . . . . . . 11
2.3.2 改良型偏移式卡爾曼濾波器. .. . . . . . . . . . 12
2.3.3 擴展式卡爾曼濾波器. . . . . . . . . . . . . . . . . 14
3 使用基於IMM 演算法之卡爾曼濾波器抑制非視線傳播誤
差. . . . . . . . . . 17
3.1 基於TOA 與RSS 量測值之卡爾曼濾波器分析與比
較. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17
3.2 利用IMM 結合TOA 與RSS 量測值之資料融合架
構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4 電腦模擬與分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.1 超寬頻系統之非視線傳播模型. . . . . . . . . . . . . . . 27
4.2 單一基地台估測結果之模擬與分析. . . . . . . . . . . 30
4.3 結合所有基地台定位結果之模擬與分析. . . . . . . 33
5 結論與建議. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

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