Comparative analysis of WNG-DF compromising beamformers

TL;DR

This paper compares different beamformer designs that balance white-noise gain and directivity, finding that robust superdirective and tunable beamformers perform best and are more practical for joint feature optimization.

Contribution

It provides a comparative analysis of beamformers for joint white-noise gain and directivity, highlighting the effectiveness of robust superdirective and tunable methods.

Findings

Robust superdirective and tunable beamformers outperform others.

These two methods produce nearly identical results across metrics.

They offer practical continuous compromise between objectives.

Abstract

This work studies beamformers designed to achieve multiple characteristics simultaneously, specifically those compromising white-noise gain and directivity factor. We compare methods explicitly designed for these joint features against those obtained by combining specific single-task beamformers. Through simulations, we demonstrate that the robust superdirective and the tunable beamformers yield the best results among those studied. Notably, these two methods produced nearly identical outputs across all evaluated metrics. These two are also more practical, continuously compromising between the two objectives.