在定位演算法之中，影響定位精準度的因素有幾種，其中以非直視路徑 (Non-Line of Sight，NLOS) 誤差影響最鉅。當行動台與基地台訊號傳輸路徑上有障礙物或建築物阻隔時，會造成訊號抵達時間的延遲，因此使得訊號抵達時間產生正向偏移誤差，當我們利用此含有 NLOS 誤差資訊做定位時，便會使得系統的定位精準度大大降低。本文提出抑制 NLOS 誤差的訊號抵達時間 (Time of Arrival，TOA) 定位演算法，首先利用最小平方支援向量機 (Least Squares-Support Vector Machine，LS-SVM)搭配粒子群最佳化演算法 (Particle Swarm Optimization，PSO)，進行參數最佳化選擇並建立訊號傳播距離估測模型。接著利用已建立的距離估測模型估測行動台與基地台之間的距離，此估測的距離能有效的降低 NLOS 誤差。再來以估測距離與量測距離之間的差値給定適當權重因子値，之後定義出權重型目標函數，最後利用最佳化演算法，可以針對權重型目標函數進行行動台位置之最佳估測，有效的縮小定位誤差範圍，進而提高定位精確度。針對超寬頻 (Ultra-Wideband，UWB) 環境，電腦模擬結果可知，利用 LS-SVM 與 PSO 的距離估測能有效抑制 NLOS 所造成的量測距離之誤差，其距離估測值能接近真實傳播距離並且比起量測距離在統計特性上之平均值與變異數都有較好的表現。且藉由結合 LS-SVM 與 PSO 的距離估測以及權重型目標函數最佳化的定位演算法架構，在ㄧ個或多個基地台遭受 NLOS 傳播影響的情形之下，能有效降低 NLOS 距離誤差，達到提高定位的精準度的目的。

One of the major problems encountered in wireless location is the effect caused by non-line of sight (NLOS) propagation. When the direct path from the mobile station (MS) to base stations (BSs) is blocked by obstacles or buildings, the signal arrival times will delay. That will make the signal measurements include an error due to the excess path propagation. If we use the NLOS signal measurements for localization, that will make the system localization performance reduce greatly. In the thesis, a time-of-arrival (TOA) based location system with NLOS mitigation algorithm is proposed. The proposed method uses least squares-support vector machine (LS-SVM) with optimal parameters selection by particle swarm optimization (PSO) for establishing regression model, which is used in the estimation of propagation distances and reduction of the NLOS propagation errors. By using a weighted objective function, the estimation results of the distances are combined with suitable weight factors, which are derived from the differences between the estimated measurements and the measured measurements. By applying the optimality of the weighted objection function, the method is capable of mitigating the NLOS effects and reducing the propagation range errors. Computer simulation results in ultra-wideband (UWB) environments show that the proposed NLOS mitigation algorithm can reduce the mean and variance of the NLOS measurements efficiently. The proposed method outperforms other methods in improving localization accuracy under different NLOS conditions.

誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
圖目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
表目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
1 緒論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 研究背景與動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 論文結構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 LS-SVM 抑制非視線誤差之TOA 定位演算法. . . . . . . . . . . . . . . . 5
2.1 最小平方支援向量機(LS-SVM) 回歸模型. . . . . . . . . . . . . . . . 6
2.2 粒子群最佳化(PSO) 演算法. . . . . . . . . . . . . . . . . . . . . . . 7
2.3 LS-SVM 抑制非視線誤差演算法. . . . . . . . . . . . . . . . . . . . . 11
2.3.1 LS-SVM 距離估測模型之建立. . . . . . . . . . . . . . . . . . 11
2.3.2 PSO 參數最佳化之LS-SVM 回歸模型. . . . . . . . . . . . . 17
2.3.3 利用LS-SVM 模型抑制NLOS 誤差之距離估測法. . . . . . . 19
2.4 權重型目標函數定位法. . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4.1 權重型目標函數. . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4.2 權重因子給定法. . . . . . . . . . . . . . . . . . . . . . . . . 21
3 電腦模擬與分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1 超寬頻訊號模型與相關參數設定. . . . . . . . . . . . . . . . . . . . . 25
3.1.1 超寬頻訊號模型. . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1.2 PSO 參數設定. . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2 抑制非視線傳播距離誤差之模擬. . . . . . . . . . . . . . . . . . . . . 27
3.2.1 模擬二個基地台所量測TOA 有NLOS 誤差情況. . . . . . . . 30
3.2.2 模擬ㄧ個基地台所量測TOA 有NLOS 誤差情況. . . . . . . . 32
3.2.3 模擬所有基地台所量測TOA 皆有NLOS 誤差情況. . . . . . . 39
3.2.4 模擬所有基地台所量測TOA 皆無NLOS 誤差情況. . . . . . . 43
3.3 具非視線誤差抑制定位之模擬. . . . . . . . . . . . . . . . . . . . . . 45
3.3.1 使用不同數目訓練資料之定位模擬. . . . . . . . . . . . . . . . 47
3.3.2 具非視線誤差抑制之多基地台定位之模擬. . . . . . . . . . . . 47
4 結論與建議. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

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