本論文對於存在無線訊號的非視線傳播 (Non-Line of Sight, NLOS) 影響，提出結合偏移式卡爾曼濾波器 (Biased Kalman Filter, BKF) 與 NLOS-Discarding 機制之交互式多模演算法 (Interacting Multiple Model, IMM) 架構，對非視線傳播所造成之誤差進行抑制，並探討在訊號抵達時間無線定位法的定位效能改善。以訊號傳遞時間為基礎之無線定位系統中，非視線傳播會造成接收時間延遲，嚴重影響定位之精準度。在抑制 NLOS 的方法之中，建立訊號統計分佈並與量測值進行假設檢定比對，須仰賴事先建立之環境統計分佈。由於含有 NLOS 成份的訊號標準差會遠大於一般的量測誤差，因此可以使用其標準差進行假設檢定以鑑別視線狀態，根據此特性使用偏移式卡爾曼濾波器 (Biased Kalman Filter, BKF) 抑制 NLOS 誤差，可結合滑動視窗並利用平滑量測值之標準差，鑑別目前視線狀態，並以回授標準差計算值與鑑別結果切換偏移/非偏移 (biased/unbiased) 卡爾曼濾波器處理，但估測值仍會受到量測值之 NLOS 少許影響。在使用卡爾曼濾波器搭配 NLOS-Discarding (KF-D) 機制的方法之中，針對量測訊號與前一個狀態之估測進行比較，當偏差值大於臨界值則假設該量測訊號受到 NLOS 影響，此方法相當於直接利用在 LOS 狀態的良好估測值進行狀態預測，因此排除了 NLOS 的影響。但此方法必須有已知的 LOS 階段提供狀態估測值，此外，行動台在 NLOS 階段的速度或加速度若有變化，卡爾曼濾波器的估測值將因為 discarding 的影響，在行動台狀態向量沒有適當更新的情況下，造成嚴重之抑制誤差。由於 BKF 與 KF-D 可互相補其缺點，為了同時保有兩者之優勢，我們提出基於 IMM 的訊號估測架構，將 BKF 與 KF-D 做為架構中的濾波器處理模組，搭配模組機率計算單元，以使估測結果有效結合 BKF 與 KF-D 的處理優勢。本論文所提出之 IMM 架構中，利用 BKF 結合滑動視窗對量測訊號進行平滑，以計算其標準差並進行假設檢定，鑑別目前之視線狀態，其結果同時傳遞至兩個濾波器與模組機率計算單元， BKF 依該結果切換偏移/非偏移之卡爾曼濾波器處理， KF-D 也根據視線狀態判斷是否接收來自 BKF 之估測值，模組機率計算單元也藉由鑑別結果更新目前兩個濾波器模組之權重，以得到最佳的訊號傳播時間估測值。論文中以超寬頻 (Ultra-Wideband, UWB) 訊號傳播環境進行模擬，並探討無線定位系統之中基地台受到 NLOS 影響時，基於 IMM 架構的 NLOS 抑制結果，並與單獨使用 BKF 或 KF-D 方法的結果進行比較，由模擬結果可以看到，本論文提出之 IMM 架構能夠有效抑制 NLOS 的影響，並提升定位估測與追蹤之精準度。

In the thesis, we propose a non-line of sight (NLOS) mitigation approach based on the interacting multiple model (IMM) algorithm. The IMM-based structure, composed of a biased Kalman filter (BKF) and a Kalman filter with NLOS-discarding process (KF-D), is capable of mitigating the ranging error caused by the NLOS effects, and therefore improving the performance and accuracy in wireless location systems. The NLOS effect on signal transmission is one of the major factors that affect the accuracy of the time-based location systems. Effective NLOS identification and mitigation usually count on pre-determined statistic distribution and hypothesis assumption in the signals. Because the variance of the NLOS error is much large than that of measurement noise, hypothesis testing on the LOS/NLOS status can be formulated.The BKF combines the sliding window and decides the status by using hypothesis testing. The calculated variance and the detection result are used in switching between the biased and unbiased modes in the Kalman filter. In the contrast, the KF-D scheme identifies the NLOS status and tries to eliminate the NLOS effects by directly using the estimated results from the LOS stage. The KF-D scheme can achieve reasonably good NLOS mitigation if the estimates in the LOS status are obtained. Due to the discarding process, changes of the state vector within the NLOS stage are possibly ignored, and will cause larger errors in the state estimates. The BKF and KF-D can make up for each other by formulating the filters in an IMM structure, which could tune up the probabilities of BKF and KF-D. In our approach, the measured data are smoothed by sliding window and a BKF. The variance of data and the hypothesis test result are passed to the two filters. The BKF switches between the biased/unbiased modes by using the result. The KF-D may receive the estimated value from BKF based on the results. The probability computation unit changes the weights to get the estimated TOA values.
With the simulations in ultra-wideband (UWB) signals, it can be seen that the proposed IMM-based approach can effectively mitigate the NLOS effects and increase the accuracy in wireless position.

誌謝 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
中文摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
英文摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
圖目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
表目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
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 非視線傳播訊號之鑑別與抑制方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1 無線定位演算法與距離量測模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.1 無線定位原理 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.2 訊號抵達時間定位法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.3 距離量測模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 非視線傳播訊號之鑑別 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2.1 使用卡爾曼濾波器之資料平滑 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2.2 結合滑動視窗輔助之非視線狀態鑑別 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.3 使用 NLOS-Discarding 機制之非視線狀態鑑別 . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 非視線傳播訊號之抑制 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.1 IMM 架構搭配 NLOS-Discarding 機制 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.2 偏移式卡爾曼濾波器 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4 NLOS-Discarding 機制與偏移式卡爾曼濾波器效能比較 . . . . . . . . . . . . . . . . . . 17
3 使用基於 IMM 之卡爾曼濾波器抑制非視線誤差 . . . . . . . . . . . . . . . . . . . . . . . . .22
3.1 所提出架構之抑制 NLOS 步驟說明 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 結合偏移式卡爾曼濾波器與 NLOS-Discarding 機制之 IMM 架構 . . . . . . . . 23
3.3 NLOS 抑制方法之效能與比較 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4 無線定位系統中存在 NLOS 誤差之狀況與探討 . . . . . . . . . . . . . . . . . . . . . . . . . .36
4.1 超寬頻無線之訊號傳播模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.2 所有基地台遭皆無受到 NLOS 影響之定位 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.3 部分基地台遭受到 NLOS 影響之定位結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.3.1 Case1 : LOS-NLOS-LOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.3.2 Case2 : LOS-NLOS-LOS之NLOS階段速度改變. . . . . . . . . . . . . . . . . . . . . . . . . 40
4.3.3 Case3 : NLOS-LOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.3.4 Case4 : NLOS-LOS-NLOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.4 所有基地台遭皆受到 NLOS 影響之定位 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.4.1 Case5 : LOS-NLOS-LOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.4.2 Case6 : NLOS-LOS-NLOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5 結論與未來方向 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .50
5.1 結論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.2 未來工作與展望 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
參考文獻 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

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