Sidelobe Suppression for Robust Beamformer via The Mixed Norm Constraint

TL;DR

This paper introduces a mixed norm constraint for beamforming that enhances sidelobe suppression, improves interference nulling, and maintains robustness against DOA mismatch, outperforming standard sparse constraint methods.

Contribution

It proposes a novel mixed norm constraint for beamformer design that better matches the beam pattern and balances mainlobe density with sidelobe sparsity.

Findings

Lower sidelobe levels achieved

Deeper nulls for interference

Enhanced robustness against DOA mismatch

Abstract

Applying a sparse constraint on the beam pattern has been suggested to suppress the sidelobe of the minimum variance distortionless response (MVDR) beamformer recently. To further improve the performance, we add a mixed norm constraint on the beam pattern. It matches the beam pattern better and encourages dense distribution in mainlobe and sparse distribution in sidelobe. The obtained beamformer has a lower sidelobe level and deeper nulls for interference avoidance than the standard sparse constraint based beamformer. Simulation demonstrates that the SINR gain is considerable for its lower sidelobe level and deeper nulling for interference, while the robustness against the mismatch between the steering angle and the direction of arrival (DOA) of the desired signal, caused by imperfect estimation of DOA, is maintained too.