A Fast Algorithm for Linear Constrained Robust Capon Beamforming Based on Iterative Gradient Method

 In order to reduce the amount of calculation of the linearly constrained robust adaptive beamforming algorithm, so that it could be applied to the real-time signal processing application scenarios of single-snapshot updating, this paper proposed a fast algorithm for linearly constrained robust Capon beamforming(LCRCB) based on single-snapshot updating and iterative gradient method, with the time complexity optimized from O(Μ³) to O(M²). The algorithm used rank-1 updating to maintain the required inverse matrix, calculated the linear constraint part of the weight, and used the iterative gradient method to update the adaptive part of the weight. The two parts are scaled and added according to the constraint to obtain the weight. Numerical simulation shows that the algorithm converges quickly and has almost the same performance as the original LCRCB.