Joint optimization of microphone array geometry, sensor directivity pattern, and beamforming parameters for linear superarrays

TL;DR

This paper introduces a comprehensive optimization framework for linear superarrays that jointly optimizes array geometry, element directivity, and beamforming filters to enhance beamforming performance across frequencies and directions.

Contribution

It presents a novel joint optimization method using genetic algorithms and series expansion to improve linear superarray design over existing approaches.

Findings

Lower approximation error compared to conventional LSAs.

More stable and improved directivity factor and white noise gain.

Enhanced beamforming performance across frequency band and steering range.

Abstract

Linear superarrays (LSAs) have been proposed to address the limited steering capability of conventional linear differential microphone arrays (LDMAs) by integrating omnidirectional and directional microphones, enabling more flexible beamformer designs. However, existing approaches remain limited because array geometry and element directivity, both critical to beamforming performance, are not jointly optimized. This paper presents a generalized LSA optimization framework that simultaneously optimizes array geometry, element directivity, and the beamforming filter to minimize the approximation error between the designed beampattern and an ideal directivity pattern (IDP) over the full frequency band and all steering directions within the region of interest. The beamformer is derived by approximating the IDP using a Jacobi-Anger series expansion, while the array geometry and element directivity are optimized via a genetic algorithm. Simulation results show that the proposed optimized array achieves lower approximation error than conventional LSAs across the target frequency band and steering range. Additionally, its directivity factor and white noise gain demonstrate more stable and improved performance across frequencies and steering angles.