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研究生:徐均
研究生(外文):HSU,CHUN
論文名稱:應用於無線通訊之適應性天線
論文名稱(外文):Applications of Adaptive Antenna for Wireless Communications
指導教授:蘇英俊張安成張安成引用關係
學位類別:博士
校院名稱:國防大學中正理工學院
系所名稱:國防科學研究所
學門:軍警國防安全學門
學類:軍事學類
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:81
中文關鍵詞:波束構成方位估測適應性陣列天線
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智慧型天線將陣列天線技術引入無線行動通訊領域,將天線陣列與訊號處理技術相結合,其中最重要的技術之一就是適應性陣列天線。適應性波束構成與高解析度方位估測則為適應性陣列訊號處理中兩項最重要的領域。大多數的方法係基於對訊號傳輸模型與天線陣列特性假設精確已知,實際上因不良的天線校正、失真扭曲的天線形狀、訊號源的散射或衰落影響、通道環境的非均質性等造成天線型態的未知與不確定性,所以方位估測技術需要在不完美陣列甚或未知通道特性下具有強健的能力,此強健能力即為對天線模型的擾動現象較不具敏感性。適應性波束構成器則需對參考訊號資料量不足及指向誤差環境下具有強健適應性能力。
首先,在具有位置擾動之不完美天線陣列模型與有色雜訊環境下,本論文提出在空間域順向線性預測器架構下結合使用迭代式 演算法以執行所欲訊號方位估測,對於動態目標之方位估測精確度明顯優於傳統之迭代最小平方演算法,且無論在靜態或動態目標之方位估測與追蹤性能上,於對抗模型化誤差、位置擾動與有色雜訊等總體影響具有極強健能力。其次,在波束構成技術應用範圍上,本論文針對存在指向誤差與干擾訊號之最小變異數無失真響應與基於特徵空間傳統波束構成器,提出一最大輸出強度校正估測法,可於較少訓練序列資料下精確估測出指向誤差,於校正後重建正確導引向量,能有效提昇波束構成器輸出性能。
對於處理分碼多重進接訊號之傳統最小變異數無失真響應波束構成器應用,在對抗指向誤差、有限取樣數及多重進接干擾之輸出性能改進上,我們提出一種極有效率之導函數多項式求根法,可快速、精確地估測與校正指向誤差,改善了傳統頻譜搜尋方式之計算處理負荷及傳統多項式求根法之估測偏差,於配合展頻碼匹配濾波器之使用下,可免於原本須小於陣列元件數之訊號源數目限制。最後,本論文對於由訊號與干擾源本地散射現象所形成之多重路徑環境下,於對抗角度擴散與所欲訊號消除影響之廣義旁波瓣消除波束構成器應用,提出一結合子陣列技術與適應性遺忘因子迭代最小平方技術之適應性遺忘因子子陣列迭代最小平方波束構成器,可免除於傳統方法因遺忘因子起始值選擇之不穩定性,並具有比傳統方法更為強健之權重調適能力,可有效提昇輸出性能並減少整體計算負荷。
The smart antennas introduce the technique of antenna array into the field of wireless mobile communications which leads to the combination of antenna array and communication signal processing. One of the most important techniques for the smart antennas is the adaptive antenna array. For the adaptive array processing, adaptive beamforming and high-resolution direction-of-arrival (DOA) estimation are the two most important areas. Most of all the approaches for these two areas are based on the assumption of that the signal propagation model and antenna array characteristics are precisely known. But in practical situations, severe degradation is created by the impact of mismatches between the presumed and actual characteristics of the propagation medium and the receiving antenna, which also includes the array manifold mismodelling. Such mismatches could be induced by imperfect array calibration, distorted antenna shape, source local scattering, multipath propagation effects and environmental inhomogeneities. Under this circumstance, the DOA estimation techniques require to possess the robust capability against these mismatches, in which the robustness means it is insensitive to the perturbations of antenna model. For the adaptive beamformers, the requirement is the robustness and adaptive capability against the effect of insufficient data of reference signal and the pointing errors.
First of all, under the environments of imperfect array with position perturbation and colored noise, this dissertation investigates the application of recursive algorithm to a forward linear predictor for DOA estimation of wireless communications. The proposed approach can deal with parameter’s uncertainty by minimizing the worst possible amplification of perturbations from imperfect array and noise signals. In comparison with the conventional algorithms for the DOA estimating performance, the proposed approach is consistently close to optimum from low to high signal-to-noise ratio (SNR) for static and moving source signals. Secondly, in the beamforming areas, this dissertation proposed an efficient technique for adaptive minimum variance distortionless response (MVDR) and eigenspace-based (ESB) beamforming with robust capabilities. To avoid the loss of degrees of freedom in suppressing undesired interferers, we proposed a maximum output magnitude searching technique to estimate the incident angle of the desired signal by using MVDR beamforming. Based on the calibrated result, the MVDR and ESB with the proposed robust technique can mitigate pointing error and product almost the same convergence speed as the MVDR and ESB beamformers under correct steering, respectively.
For the application of conventional MVDR beamformer in dealing with the code division multiple access (CDMA) signals, an efficient derivative polynomial rooting calibration method that is robust in the scenarios of pointing errors, finite samples and multiple access interference (MAI) is proposed. Calibration process is done by estimating the pointing error of the desired signal. The main skill is to root the first-order derivative of the cost function instead of direct rooting it. In comparison with the conventional polynomial rooting method, the proposed approach can improve the root-selecting computation and reduced the bias of pointing error estimate due to the effect of noise and MAI. Finally, in concerning with the multipath environments created by sources local scatterings, this dissertation proposed an efficient subarray recursive least square (SRLS) beamformer with adaptive forgetting factor (AF) in which it can be adjusted in each adaptation process of the partitioned subweight. The proposed approach not only performs the adaptive beamforming, but also adaptively changes the forgetting factors to track the environmental changes. It is reliable in providing better performance than the conventional RLS-based algorithms but on the reduced computational burden basis.
誌謝…………………………………………………………………………………...ii
摘要…………………………………………………………………………………..iii
ABSTRACT…………………………………………………………………………..v
目錄………………………………………………………………………………….vii
表目錄 ………………………………………………………………………………...x
圖目錄 ………………………………………………………………………………..xi
1. 緒論 ……………………………………………………………………………….. 1
1.1 研究動機與目的 1
1.2 文獻探討 4
1.3 論文架構 9
2. 強健型迭代 到達方位角度估測……………………………………………... 10
2.1 簡介 10
2.2 問題形成 12
2.2.1 陣列資料模型 12
2.2.2 順向線性預測器.................................................................................. 12
2.3 RLS演算法 13
2.4 迭代式 演算法 14
2.5 DOA估測方式 17
2.6 模擬結果 18
2.7 結語 20
3. 最大輸出強度校正法之適應性波束構成……………………………………… 27
3.1 簡介 27
3.2 問題形成 28
3.3 所提出使用最大輸出強度校正之波束構成器 31
3.4 模擬結果 34
3.5 結語 39
4. 強健型求根值最小變異數無失真響應波束構成……………………………… 40
4.1 簡介 40
4.2 背景說明 41
4.2.1 訊號模型 41
4.2.2 最小變異數無失真響應波束構成器 42
4.3 多項式求根校正估測法 44
4.3.1 傳統多項式求根估測法 44
4.3.2 所提出導函數多項式求根估測法 49
4.4 模擬結果 52
4.5 結語 54
5. 適應性遺忘因子子陣列迭代最小平方波束構成……………………………… 56
5.1 簡介 56
5.2 背景說明 57
5.2.1 陣列資料模型 57
5.2.2 以廣義型旁波瓣消除器為基之適應性遺忘因子迭代最小平方演算法 58
5.3 所提出適應性遺忘因子子陣列迭代最小平方演算法 59
5.4 模擬結果 64
5.5 結語 68
6. 結論與未來研究方向…………………………………………………………… 69
6.1 結論 69
6.2 未來研究方向 70
參考文獻……………………………………………………………………………. 71
論文發表……………………………………………………………………………. 79
自傳…………………………………………………………………………………. 81
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