Worst-case based robust adaptive beamforming for general-rank signal models using positive semi-definite covariance constraint

摘要

In this paper, we develop a new approach to the robust beamforming for general-rank signal models. Our method is based on the worst-case performance optimization using a semi-definite constraint on the mismatched signal covariance matrix. The resulting robust adaptive beamforming problem is solved using iterative semi-definite programming (SDP) with a guarantee of convergence. The performance improvement of the proposed approach over the current robust adaptive beamforming techniques developed for the general-rank signal environments is confirmed by simulation results.