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研究生： 田凱元
Kai-yuan Tian
論文名稱： 精準方位估測演算法設計
The Algorithm DesignThe Algorithm Design of High Resolution Direction of Arrival
指導教授： 陳永芳
Yung-fang Chen
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 通訊工程學系在職專班
Executive Master of Communication Engineering
畢業學年度： 98
語文別： 中文
論文頁數： 64
中文關鍵詞： 訊號方位 、方位估測
外文關鍵詞： DOA, ESPRIT, Matching Pursuit, MUSIC
相關次數： 點閱：11  下載：0 
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    • 此篇論文所介紹的是精準估測訊號來源方位角的方法，現今較常使用估測方位角度的演算法大概分為兩種，一種為ESPRIT演算法[9]及MUSIC演算法[17]，而我們將精準定義為誤差角度於1.5度間，為使估測角度與原訊號來源角度誤差更小，將原估測方位角，再加上運算複雜度相對較高的以Matching Pursuit(匹配追蹤)為基礎的兩階段演算法[6]，加以精算得出較精確的結果。
    論文中也比較了ESPRIT、MUSIC及Matching Pursuit三種演算法，設定於各項不同環境參數中：天線陣列數、快照數據資料等，估測訊號方位的精準度及效益，並依此三種演算法特性進行精確方位估測的搭配及提升精準度。

    In this thesis, a high resolution angle of arrival etimation algorithm is developed. There are two popular methods used to estimate the arrival angle of a source, one is the ESPRIT [9] algorithm and the other is the MUSIC [17] algorithm. If the estimation errors are smaller than 1.5 degrees, it is defined as the precision estimation. In order to improve the estimation error, the initial estimates of the ESPRIT or the MUSIC algorithm can be further refined by the Matching Pursuit technique which has a higher computational complexity. The two-stage algorithm is thus designed and the complexity is reduced with comparable performance compared to the pure Matching Pursuit technique.
    The thesis compares the ESPRIT, the MUSIC, and the Matching Pursuit algorithms in different scenarios such as the number of antenna elements and the number of the snapshots …,etc. The performance of the estimates by the three methods is displayed through simulation results. By the combination of these algorithms, how to achieve the precision estimation is discussed by the trade off between performance and complexity.

    目錄 摘要 i Abstract ii 目錄 iii 圖目錄 vi 表目錄 viii 第一章 緒論 1 1.1 研究動機 1 1.2 研究目的 3 1.3 論文架構 4 第二章 方位估測演算法設計架構 5 2.1 方位估測模擬資料架構 5 2.2 使用Esprit演算法估算[9] 7 2.3 使用多重訊號分類MUSIC演算法初估[14] 14 2.3.1 MUSIC演算法說明 15 2.3.2 MUSIC演算法摘要 18 2.4 以Matching Pursuit為基礎的二階段演算法 19 2.4.1 匹配追蹤演算法(Matching Pursuit) 20 2.4.1.1 正交化匹配追蹤法[3] 21 2.4.1.2 彈性樹狀基底搜尋正交化匹配演算法(Flexible Tree-search-based Orthogonal Matching Pursuit) 23 2.4.2 最小平方估測集合(Bank of Least Squares Estimators)[6] 25 2.5 精準方位估測演算法設計運算流程圖 27 第三章 訊號方位估測演算法模擬比較 28 3.1 相同訊雜比及快照資料數量下，天線元件數量與估測效益間關係。 29 3.1.1 訊雜比(SNR)：0 dB、快照資料數量：10組 29 3.1.2 訊雜比(SNR)： 6 dB、快照資料數量：10組 30 3.1.3 訊雜比(SNR)： 12 dB、快照資料數量：10組 31 3.2 不同天線元件數量下，訊雜比與估測效益間關係。 32 3.2.1 天線元件數量：8組、快照資料數量：10組 32 3.2.2 天線元件數量：12組、快照資料數量：10組 33 3.2.3 天線元件數量：16組、快照資料數量：10組 34 3.3 各種演算法複雜度比較 35 3.4 討論 39 第四章 精準方位估測演算法模擬 43 4.1 以8組天線元件進行模擬運算 44 4.1.1 以ESPRIT演算法作為第1步估算 44 4.1.2 MUSIC演算法作為第1步估算 45 4.2 以10組天線元件進行模擬運算 46 4.2.1 以ESPRIT演算法作為第1步估算 46 4.2.2 以MUSIC演算法作為第1步估算 47 4.3 以12組天線元件進行模擬運算 48 4.3.1 以ESPRIT演算法作為第1步估算 48 4.3.2 以MUSIC演算法作為第1步估算 49 4.4 以16組天線元件進行模擬運算 50 4.4.1 以ESPRIT演算法作為第1步估算 50 4.4.2 以MUSIC演算法作為第1步估算 51 4.5 小結 52 第五章 總結與延申應用 53 第六章 參考資料 54

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