Single-Snapshot Gridless 2D-DoA Estimation for UCAs: A Joint Optimization Approach

TL;DR

This paper introduces a novel, efficient joint optimization framework for gridless 2D-DOA estimation from a single snapshot using a UCA, overcoming computational and robustness limitations of existing methods.

Contribution

It proposes a unified optimization approach with an inexact ALM that avoids semidefinite programming for single-snapshot 2D-DOA estimation.

Findings

Provides robust, high-resolution 2D-DOA estimates from a single snapshot.

Demonstrates efficiency by eliminating semidefinite programming.

Shows superior performance in challenging array signal processing scenarios.

Abstract

This paper tackles the challenging problem of gridless two-dimensional (2D) direction-of-arrival (DOA) estimation for a uniform circular array (UCA) from a single snapshot of data. Conventional gridless methods often fail in this scenario due to prohibitive computational costs or a lack of robustness. We propose a novel framework that overcomes these limitations by jointly estimating a manifold transformation matrix and the source azimuth-elevation pairs within a single, unified optimization problem. This problem is solved efficiently using an inexact Augmented Lagrangian Method (iALM), which completely circumvents the need for semidefinite programming. By unifying the objectives of data fidelity and transformation robustness, our approach is uniquely suited for the demanding single-snapshot case. Simulation results confirm that the proposed iALM framework provides robust and high-resolution, gridless 2D-DOA estimates, establishing its efficacy for challenging array signal processing applications.