A Generalized Loading Algorithm for Adaptive Beamforming in Uniform Linear Array

ABSTRACT: The aim of this work is focused on designing a new generalized loading approach for Uniform Linear Array (ULA). This method achieves robust adaptive beamforming against direction-of-arrival (DOA) mismatches by shaping the directional response of the adaptive Uniform Linear Array. To achieve this purpose, we consider an ULA with sensors, which are located at half wavelength spacing. The desired spatial signal impinges from a specific direction, such that the presumed direction has a mismatch with the true direction. Furthermore we assume that the desired signal components are present in the beamformer training data snapshots and that the data sample size is limited. Therefore, we desire to develop a robust adaptive beamformer to improve the performance against inaccuracies caused by limited sample size and the look direction mismatch by shaping the directional response of the array. N The ability of the directional response shaping (DRS) will follow the modified conventional loading methods for an adaptive ULA. However, it stresses on the range of specified direction (cut off angle) in the presence of undesired interferences. In this technique, a general loading matrix is considered which is derived from a weight function. This matrix is added to the estimated correlation matrix, such that the directional response of the beamformer is shaped. Also, the loaded matrix minimizes the output power of the beamformer. By using the loaded matrix, beamformer weight vector will approach to an optimal value regarding the output SINR. The weight function can be chosen to further suppress the interferences by making the weight large in the vicinity of DOA’s of interferences. To demonstrate the capability of the proposed method, it is compared with some of the well-known methods such as Sample Matric Inversion (SMI), Loaded Diagonally Sample Matrix Inversion (LSMI), Robust Capon Beamformer (RCB), Iterative Minimum Variance Beamformer (IRMVB (Li’s)) algorithms. The results clarify that, convergence of our method to optimal SINR in different conditions is superior. Keywords: Adaptive Beamformer, Uniform Linear Array (ULA), Mismatch, Interferences, Generalized Loading matrix, Correlation Matrix. …………………………………………………………………………………………………………………………