如果在一些像是訊號源或感測陣列被擾亂的環境假設下，適應性陣列波束構成方法會有性能下降，實際上，如果所欲訊號被用作訓練序列，則適應性陣列的性能甚至可能對於假設和實際訊號的指向向量間的微小不匹配相當靈敏，這樣的不匹配情形可能發生於像是指向誤差和增益擾動及位置擾動的不完美陣列校正。
本論文處理一個共變異整型最小平方技術的強健性適應性波束構成器，它使用最小化權重總誤差的變異值，並限制估測誤差的共變異值使得估測誤差的共變異值之動態範圍和頻譜形狀(Spectral Shape)，用以對輸出的訊號對干擾加雜訊比(Signal-to-Interference-plus-Noise Ratio, SINR)做最大化。共變異整型最小平方波束構成器直接改善了傳統最小平方波束構成器在低輸入訊雜比(Signal-to-Noise Ratio, SNR)且低訊號干擾比(Signal-to-Interference Ratio, SIR)的性能。換言之，共變異整型最小平方波束構成器在強干擾之環境下，相對於其他方法，可以達到較快速的收斂速度及對導引向量不匹配之情況有較低的陣列靈敏度。
然而，共變異整形最小平方波束構成器的主要缺點是在高訊號干擾比時會有性能些微下降的情形；由於這個原因我們也提出一個修正型的共變異整型最小平方波束構成器來增加在高輸入訊擾比時的性能。基於特徵空間波束構成器已證明對傳統波束構成器來說有較快速收歛和對導引向量誤差有較低的靈敏度。藉著投影共變異整型最小平方波束構成器從接收資料相關矩陣的特徵結構建構的權重向量子空間，修正型共變異整型最小平方波束構成器的權重向量可被求得。共變異整型最小平方波束構成器和修正型共變異整型最小平方波束構成器對於所欲訊號和干擾有相同的響應，然而，修正型共變異整型最小平方波束構成器的權重向量有較小的範數和產生一個較低的輸出雜訊功率；最後使用一些電腦模擬來證明所提出方法的效能。

Adaptive array beamforming methods are known to degrade if some of underlying assumptions on the environment, sources, or sensor array become violated. In particular, if the desired signal is presented in training snapshots, the adaptive array performance may be quite sensitive even to slight mismatches between the presumed and actual signal steering vectors. Such mismatches can occur as a result of pointing errors and imperfect array calibration gain perturbation an position perturbation.
This thesis deals with adaptive array beamforming based on covariance shaping least-squares (CSLS) technique with robust capabilities. It uses the scheme to minimize the weighted total error variance in the observations subject to a constraint on covariance of the estimation error, so that we control the dynamic range and spectral shape of the covariance of the estimation error to maximize the output signal-to-interference-plus- noise ratio (SINR). The CSLS beamformer is directed at improving the performance of the conventional least-squares beamformer at low to moderate input signal-to-noise ratio (SNR) and low signal-to-interference (SIR). In other words, if CSLS beamformer in the circumstances which interference with strong power, it can achieve the faster convergence speed and less array sensitivity to the steering vectors mismatches than others.
However, the major drawback in utilizing the CSLS beamformer causes performance slight degradation in the case of large input SIR. For this reason, we also proposed a modified covariance shaping least-squares (MCSLS) beamformer to enhance the performance at high input SIR. It has shown that eigenspace-based (ESB) beamformer demonstrate the advantages of fast convergence speed and less sensitivity to steering vector errors over conventional beamformer. The weight vector of the MCSLS beamformer is found by projecting the CSLS weight vector subspace constructed from the eigenstructure of the received data correlaton matrix. The MCSLS and CSLS beamformers have the same responses to the desired signal and interferers. However, the weight vector of the MCSLS beamformer has a smaller norm and generates a lower output noise power. Several computer simulation examples are provided for illustrating the effectiveness of the proposed techniques.

摘要 i
Abstract i
目錄 iii
圖目錄 v
第一章 緒論 1
1.1 研究背景及動機 1
1.2 研究目的 1
1.3 國內外相關研究 2
1.4 論文架構與貢獻 4
第二章 智慧型天線系統架構 5
2.1 智慧型天線的原理 5
2.2 陣列排列形狀 6
2.2.1 均勻線性陣列 7
2.2.2 均勻平面陣列 7
2.3 智慧型天線的種類 10
2.3.1 切換波束式系統 10
2.3.2 適應性天線 11
第三章 研究方法 14
3.1 問題形成 15
3.1.1 性能指標 15
3.1.2 陣列增益擾動 16

3.1.3 陣列位置擾動 17
3.1.4 指向誤差問題 18
3.2 基於特徵空間波束構成器(Eigenspace-Based Beamformer, ESB) 19
3.2.1 特徵空間方法推導 20
3.2.2 電腦模擬與基本分析 20
3.3 最小平方波束構成器 22
3.3.1 最小平方方法推導 23
3.3.2 電腦模擬與基本分析 24
3.4 加權最小平方波束構成器 26
3.4.1 加權最小平方方法推導 28
3.4.2 電腦模擬與分析 29
3.5 共變異整型最小平方波束構成器 31
3.5.1 共變異整型最小平方方法推導 32
3.5.2 電腦模擬與基本分析 36
3.6 修正型共變異整型最小平方波束構成器 38
3.6.1修正型共變異整型最小平方方法推導 38
3.6.2 電腦模擬與基本分析 40
第四章 模擬結果 43
4.1 理想線性陣列且有正確已知的導引向量 43
4.2 理想線性陣列且有到達角度估測誤差之情形 47
4.3 非理想線性陣列在增益擾動之情況下 53
4.4 非理想線性陣列在增益擾動情況下且有到達角度估測誤差之情形 59
4.5 非理想線性陣列在位置擾動之情況下 65
4.6 非理想線性陣列在位置擾動情況下且有到達角度估測誤差之情形 71
第五章 結論 77
5.1 研究成果 77
5.2 未來展望 77
參考文獻 78

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