Beamforming Algorithm Research In Array Signal Processing

Array signal processing is an important branch of signal processing and its applicationsinvolve many areas, such as radar, sonar, navigation, communications and medical imaging.Adaptive beamforming is an important research direction in the array signal processing. Aim-ing at achieving the best receiver in some criterions and attaining the purpose of the spatialfilter, we adjust the weight vector to change the pattern of the array, which makes pointing thebeam main lobe at the desired signal, sidelobes null at the interfering signals, so that the SINRof the output can be improved.In this thesis, we focus on the study of beamforming algorithms in array signal processing,and some details are as follows:We built the mathematical model of an array antenna through modeling, and introducedsome necessary mathematical knowledge in array signal processing research, which laya good theoretical basis for the follow-up study.we presented some performance metrics commonly used in adaptive control algorithm,and compared their advantages and disadvantages.we introduced and compared two classic beamforming algorithms, named Bartlett beam-former and Capon beamformer. Then, we used MATLAB to simulate their beamformsand compared their performance under diferent circumstances.As there exist interference, noise and so many uncertainties in the actual environment, inorder to adapt to the actual environment, we proposed a robust adaptive beamforming al-gorithm based on Bayesian criteria. Taking advantage of the priori probability, Bayesianrobust adaptive beamforming algorithm can integrate the priori probability knowledgeof the direction of arrival and the observational data, which help the beamformer forma good shape of the signal. At high SNR, the Bayesian beamformer emphasis on theprocessing of the received data, which can be approximated by a minimum variance dis-tortionless response beamformer. However, at low SNR, the Bayesian beamformer is even more dependent on a priori probability of the DOA. It can form a wide main beamto tolerant the uncertainty of the DOA. Through simulations, we compared the perfor-mance of the algorithm under diferent circumstances, which shows that the algorithmis very robust to DOA uncertainty.To demonstrate the performance of the algorithm in an actual environment, we also usedactual engineering data to verify the Bayesian robust adaptive beamforming algorithm.The results were very satisfactory.