Robust Adaptive Beamforming Algorithm Based On Diagonal Loading

With the development of the third generation mobile communication system, adaptive antenna technology has received considerable attention recently. When adaptive arrays are applied to practical problems, the performances of adaptive beamforming algorithms are known to degrade severely in the presence of steering vector mismatches. Such mismatches can occur as a result of near-far spatial signature mismatch, source spreading, imperfectly calibrated arrays. Similar type of performance degradation can occur when the signal steering vector is known exactly but the training sample size is small. Therefore, the need for robust beamforming arises in many practical applications and robust approaches are known to be of primary importance in these cases.In this thesis, we analyze the factors that may affect the robustness of these methods in practical applications, and introduce some conventional algorithms, such as additional linear constraints approaches, diagonal loading and eigenspace-based beamforming approach. To account for practical problems, a novel robust adaptive beamforming based on diagonal loading approach is proposed.The diagonal loading method is a simple and efficient method for improving the robustness of adaptive beamforming. However, the main shortcoming of the method is that it is not clear how to obtain the optimal value of the diagonal loading factor based on the known level of uncertainty of the signal steering vector. In practical communication environment the performances of adaptive beamforming algorithms are known to degrade severely because of signal steering vector mismatches and small training sample size. To solve these problems, a novel robust adaptive beamformer is proposed, which combines load sample matrix inversion(LSMI) algorithm and covariance matrix estimation algorithm. In this paper, we can obtain the optimal weight vector based on the diagonal loading factor. The proposed algorithm has a low computational complexity for using the recursive method. The robust adaptive beamforming provides an improved robustness and makes the mean output array SINR consistently close to the optimal one. The method improves robustness and makes the mean output array SINR consistently close to the optimal one. Moreover, the proposed method provides an improved robustness against signal steering vector mismatches and small training sample size.