A high-precision DOA estimator for low-frequency signals using ultra-dense small-aperture microphone arrays

Abstract

Accurate Direction of Arrival (DOA) estimation is critical for the effectiveness of Unattended Ground Sensor (UGS) systems, as it enhances sound localization, situational awareness, resource optimization, and facilitates integration with other sensor data for comprehensive monitoring. With the growing demand for lightweight and miniaturized sensors suitable for diverse environments, challenges arise in DOA estimation of low-frequency signals using dense small-aperture microphone arrays, especially under noisy conditions. Despite advancements in deep learning, both conventional and existing neural network methods struggle with this task. In this paper, we present the Multi-Resblock DOA Network (MRDNet), a novel neural network designed for precise DOA estimation of low-frequency sounds in noisy environments using small-aperture arrays. MRDNet was evaluated under simulations involving Brownian and Gaussian noise, representing wind and general background disturbances. The results demonstrate that MRDNet achieves superior accuracy, with a Mean Angular Error (MAE) of 2.797 degrees, significantly outperforming baseline methods by 24.93\%. Furthermore, we show that increasing the number of microphones within a constant array size using MRDNet effectively enhances DOA accuracy in the context of deep learning.