Robust Diagonal Loading Algorithm for Worst-Case Performance Optimization

Traditional adaptive beamforming methods are known to undergo serious performance degradation in the presence of mismatches between the assumed array response and the true array response. In this paper, we propose a robust algorithm for worst-case performance optimization which belongs to the class of diagonal loading approach, and the diagonal loading factor is computed automatically from the array observation vectors without the need of specifying any user parameters. The proposed algorithm is based on the oblique projection of the signal steering vector to mitigate the effect of noise and interference. Moreover, in order to reduce the computational cost, the weight vector is updated iteratively via the gradient descent method. The proposed algorithm provides better robustness against the signal steering vector mismatches, yields improved array output performance and has a faster convergence rate. Some simulation results are presented to compare the performance of the proposed algorithm with sample matrix inversion (SMI) algorithm