关键词: 导向矢量估计, 稳健波束形成, 序列二次规划, 线性近似, 最坏情况性能最优

Abstract:

Aiming at the probably existing performance loss and high computational complexity of the robust beamforming based on steering vector estimation with as little prior information as possible which is solved by the semi-definite relaxation (SDR) approach, a novel robust beamforming algorithm using sequential quadratic programming (SQP) is proposed. The original non-convex problem is linearly approximated to a convex subproblem using the first order Taylor's series, and the optimal solution is found out by solving the convex subproblem iteratively. Moreover, considering the mismatch of the sample covariance matrix, the SQP-WC method based on worst-case performance optimization is presented to improve the performance of the proposed SQP method. Theoretical analysis and simulation results show that the proposed SQP algorithm can converge fast and its convergence point approximates the optimal solution to the original problem, which indicates that the SQP method can effectively reduce the computational complexity compared with the SDR method, and furthermore, the SQP-WC method can effectively improve the performance of the SQP method with a small parameter.

Key words: steering vector estimation, robust beamforming, SQP, linear approximation, worst-case performance optimization