Localization of DOA trajectories -- Beyond the grid

TL;DR

This paper introduces gridless trajectory models and algorithms for DOA estimation that better capture source dynamics and improve localization accuracy over traditional grid-based methods, especially in noisy conditions.

Contribution

It proposes polynomial and bandlimited DOA trajectory models and applies gridless algorithms like SFW and NOMP for enhanced localization performance.

Findings

Improved resolution over grid-based methods

Enhanced robustness to noise

Greater computational efficiency

Abstract

The direction of arrival (DOA) estimation algorithms are crucial in localizing acoustic sources. Traditional localization methods rely on block-level processing to extract the directional information from multiple measurements processed together. However, these methods assume that DOA remains constant throughout the block, which may not be true in practical scenarios. Also, the performance of localization methods is limited when the true parameters do not lie on the parameter search grid. In this paper we propose two trajectory models, namely the polynomial and bandlimited trajectory models, to capture the DOA dynamics. To estimate trajectory parameters, we adopt two gridless algorithms: i) Sliding Frank-Wolfe (SFW), which solves the Beurling LASSO problem and ii) Newtonized Orthogonal Matching Pursuit (NOMP), which improves over OMP using cyclic refinement. Furthermore, we extend our analysis to include wideband processing. The simulation results indicate that the proposed trajectory localization algorithms exhibit improved performance compared to grid-based methods in terms of resolution, robustness to noise, and computational efficiency.