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研究生:陳泰元
研究生(外文):Tai-Yuan Chen
論文名稱:無線定位系統中結合粒子濾波器最佳化演算法之非視線傳播鑑別
論文名稱(外文):Non-Line of Sight Identification with Particle Filter Optimization Algprithm in Wireless Location
指導教授:萬欽德
指導教授(外文):Chin-Der Wann
學位類別:碩士
校院名稱:國立中山大學
系所名稱:電機工程學系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:74
中文關鍵詞:粒子濾波器分群演算法無線定位非視線傳播訊號抵達角度訊號抵達時間
外文關鍵詞:Wireless LocationTOAK-meansParticle FilterAOANLOS
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在無線通訊系統中,訊號受非視線(Non-Line-of-Sight,NLOS)傳播誤差影響而導致定位準確度嚴重降低,故判別出多少個量測訊號為直視傳播(Line-of-Sight,LOS)並同時鑑別出,有助於行動定位準確性之提升。本論文提出一種基於假設檢定(Hypothesis Testing)的遞迴演算法,利用殘餘資訊的概念去判別定位量測值中是否含有NLOS誤差,殘餘資訊的概念為利用不同組合的訊號抵達時間(Time of Arrival,TOA)及角度抵達時間(Angle of Arrival,AOA)量測資訊所估測出來的估測值與利用全部量測資訊的估測值做相減計算並跟一個估測誤差的理論值下限相除。在當量測值含有NLOS誤差的情況下,其殘餘資訊的機率分佈為一個近似自由度為一的非中央式卡方分佈不同於量測值全為LOS狀態下的機率分佈為近似自由度為一的中央式卡方分佈,我們利用LRT~(Likelihood Ratio Test)可判別出訊號量測值是否為LOS或NLOS狀態。在假設檢定演算法所使用的臨界值利用粒子濾波器最佳化演算法可以找出其最佳的值,做為假設檢定判別的依據。粒子濾波器最佳化演算法利用粒子濾波器的特性來選取遞迴演算法所需之最佳臨界值,使得用來判斷多個基地台所量測到訊號的狀態能夠更加的準確,在粒子濾波器最佳化演算法裡,本論文亦使用K-means分群法將粒子分群,藉此將搜尋最佳臨界值的過程同步處理以便能夠快速的找到最佳解。本文中針對混合TOA/AOA (Time of Arrival/Angle of Arrival)定位法,利用判別結果中具備LOS狀態基地台之量測值進行位置的估測。因為排除受到NLOS影響之基地台量測值,演算法能有效的降低NLOS所造成的定位誤差。電腦模擬結果顯示,本文所提出的演算法在基地台遭受不同程度的非直視誤差干擾情況下,其定位效能較其他演算法皆有提升,亦獲得較高的LOS基地台鑑別準確率,針對不同情況皆能獲得適用於該判斷狀態的最佳臨界值。
In wireless location systems, received signals may be influenced by non-line of sight (NLOS) propagation errors, which yield severe degradation of location accuracy.Therefore, to distinguish how many measurement signals are line-of-sight (LOS) and to identify them simultaneously will contribute to the increase of location accuracy.We propose a method based on recursive hypothesis testing algorithm, and use residual information to determine whether the NLOS errors are present in measurements. Since the probability distribution of measurements with NLOS errors is different from that of measurements without NLOS errors, a likelihood ratio test can be used in determining the LOS/NLOS status of the measurements. To search for an optimal threshold for the hypothesis testing, particle filtering optimization(PFO) is adopted. The PFO algorithm uses particle filtering to find the best threshold for determining the status of signals measured at all base stations (BSs). In the PFO algorithm, the clustering property of K-means is also used in separating particles, thereby the search of optimal threshold may be implemented in parallel.In this thesis, we focus on the hybrid TOA/AOA (time of arrical/angle of arrival) location method, in which localization only uses the LOS location measurements to calculate the location of a mobile station. Simulation results show that the proposed algorithm performs better than other algorithms which suffer from different degrees of NLOS errors. The proposed scheme also obtains higher identification rate of LOS-BSs in different situations by using the optimal thresholds for status detection.
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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 論文結構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 鑑別NLOS 誤差之定位系統. . . . . . . . . . . . . . . . . . . .. . . 4
2.1 判別非視線基地台臨界值之探討. . . . . . . . . . . . . . . . .4
2.2 基於假設檢定混合型TOA/AOA 判別非視線基地台演算法. . . . . . . 8
2.2.1 最大概似估測法. . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.2 假設檢定之殘餘測試法(Hypothesis Testing Residual Test) . . 11
2.3 粒子濾波器最佳化演算法之目標函數. . . . . . . . . . . . 17
3 粒子濾波器最佳化演算法之最佳臨界值搜尋. . . . . . . .19
3.1 粒子濾波器(Particle Filter) . . . . . . . . . . . . . . . . . . . .19
3.1.1 重要性取樣. . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.2 順序重要性取樣. . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.3 重取樣. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.1.4 粒子濾波器總結. . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 最佳臨界值之搜尋. . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2.1 K-means 分群法. . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2.2 粒子濾波器最佳化演算法. . . . . . . . . . . . . . . . . . . . 25
4 電腦模擬與分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.1 非直視情況下五個基地台之定位誤差模擬. . . . . . . 31
4.1.1 不同時間量測誤差之影響. . . . . . . . . . . . . . . . . . . . 32
4.1.2 不同角度量測誤差之影響. . . . . . . . . . . . . . . . . . . . 41
4.1.3 固定臨界值之模擬比較. . . . . . . . . . . . . . . . . . . . . . 49
4.1.4 行動台位置不同之影響. . . . . . . . . . . . . . . . . . . . . . 52
4.2 非視線基地台鑑別準確度之模擬. . . . . . . . . . . . . . . .55
4.3 K-means 分群數目之影響. . . . . . . . . . . . . . . . . . . . 57
5 結論與建議. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
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