Adaptive Array Beamforming with Fast Convergent and Robust Capabilities Under Non-ideal Environments
Date Issued
2014
Date
2014
Author(s)
Chao, Chia-Ching
Abstract
Adaptive array beamforming which can extract signals of interest from specific angles while suppress interferences and noise by adjusting weights on the array elements. And they can be classified into two types. The first type we call it conventional beamforming. For conventional beamforming, the a priori information is the direction of the desired signal, and this kind of technique is also called steered-beam beamforming, for example linearly constrained minimum variance beamformer (LCMV). In recent years, another kind of adaptive array beamforming without the knowledge of the direction of the desired signal has been widely presented. It utilizes some characteristic of the signals to achieve beamforming, for example, signal cyclostationary and thus it is called “blind” beamformer. The purpose of this thesis is mainly to develop several fast convergent and robust techniques for both steered-beam beamformer and blind beamformer using cyclostationary in order to tackle the performance degradation under non-ideal enviroments.
This thesis can be divided into two parts. Chapter 3 is the first part of this thesis. To deal with the cycle frequency error (CFE), we present an iterative averaging (IA) scheme in conjunction with average cyclic correlation matrix algorithm (ACCM) to estimate the actual cyclic correlation matrix. In addition, we apply Fully Data-Dependent method to improve the convergence of the proposed method. In this way, we would expect that the propsed method will be a fast convergent and robust beamforming techniques. We will present the simulation results to show the effectiveness of the proposed method.
From the forth chapter, we present a new sheme of the suppert vectoe machines beamformer(SVM-beamformer) which can deal with multiple signals of interest (SOIs). The advantages of using SVM-beamformer (which is use to handle single SOI) are that it can combine with the existing robust array beamforming techniques and improve their convergence and performance. (In this thesis, the fast convergence means that we can use less data to get good performance, and the performance means the array output signal-to-interference-plus-noise ratio (SINR)). Besides, we note that it can also be applied to the environment of multiple SOIs. Therefore, we combine several robust array beamforming techiques with the SVM-beamformer to deal with the problem of array beamforming under multiple SOIs and non-ideal enviroments.
Subjects
陣列信號處理
強健式陣列波束成型技術
強健式陣列多重波束成型技術
支持向量機
指引向量誤差
週期頻率誤差
隨機週期頻率誤差
有限資料點
信號同調
Type
thesis
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