A Robust Beamformer Based on Weighted Sparse Constraint

TL;DR

This paper introduces a weighted sparse constraint in beamformer design to achieve lower sidelobe levels, deeper nulls, and enhanced robustness against DOA mismatch, improving interference suppression.

Contribution

The novel weighted sparse constraint enhances beamformer performance by reducing sidelobes and increasing robustness compared to traditional MVDR methods.

Findings

Lower sidelobe levels achieved

Deeper nulls for interference suppression

Improved robustness against DOA mismatch

Abstract

Applying a sparse constraint on the beam pattern has been suggested to suppress the sidelobe level of a minimum variance distortionless response (MVDR) beamformer. In this letter, we introduce a weighted sparse constraint in the beamformer design to provide a lower sidelobe level and deeper nulls for interference avoidance, as compared with a conventional MVDR beamformer. The proposed beamformer also shows improved robustness against the mismatch between the steering angle and the direction of arrival (DOA) of the desired signal, caused by imperfect estimation of DOA.