Abstract:
This work revisits the classic robust adaptive beamforming that is widely adopted for interference suppression. A first-order method is proposed to solve the beamformers ...Show MoreMetadata
Abstract:
This work revisits the classic robust adaptive beamforming that is widely adopted for interference suppression. A first-order method is proposed to solve the beamformers for large arrays. The method uses proximal gradient descent along with Nesterov’s acceleration. It has {\mathcal{O}}\left( {{N^2}} \right) computational complexity per iteration where N is the array size. For sparse linearly constrained adaptive beamforming, the proposed method achieves performances comparable to the conjugate gradient method. For sparse robust adaptive beamforming with conic constraints, the proposed method is much more efficient than the standard interior point solver.
Published in: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 04-10 June 2023
Date Added to IEEE Xplore: 05 May 2023
ISBN Information: