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

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-de nite constraint on the mismatched signal covariance matrix. The resulting robust adaptive beamforming problem is solved using iterative semi-de nite 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 con rmed by simulation results.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Haihua Chen, Alex B. Gershman
• Keywords: Adaptive Beamforming Problem, Adaptive Beamforming Techniques, ICASSP 2011, Robust Adaptive Beamforming, Signal Processing