A Subspace-based Robust Adaptive Capon Beamforming

Adaptive beamforming suffers from performance degradation in the presence of mismatch between the actual and presumed array steering vector of the desired signal. This idea enlightens us, so we propose a subspace approach to adaptive beamforming that is robust to array errors based on minimizing MUSIC output power. The proposed method involves two steps, the first step is to estimate the actual steering vector of the desired signal based on subspace technique, and the second is to obtain optimal weight by utilizing the estimated steering vector. Our method belongs to the class of diagonal loading, but the optimal amount of diagonal loading level can be calculated precisely based on the uncertainty set of the steering vector. To obtain noise subspace needs eigen-decomposition that has a heavy computation load and knows the number of signals a priori. In order to overcome this drawback we utilized the POR (Power of R) technique that can obtain noise subspace without eigen-decomposition and the number of signals a priori. It is very interesting that Li Jian’s method is a special case where m = 1, and the proposed subspace approach is the case where m → ∞, so we obtained a uniform framework based on POR technique. This is also an explanation why the performance of the proposed subspace approach excels that of Li Jian’s method. The excellent performance of our algorithm has been demonstrated via a number of computer simulations.