Single-Snapshot Gridless 2D-DoA Estimation for UCAs: A Joint Optimization Approach
This addresses a challenging computational bottleneck in array signal processing for applications like radar or communications, though it appears incremental as it builds on existing gridless methods.
This paper tackled the problem of gridless two-dimensional direction-of-arrival estimation for uniform circular arrays from a single snapshot, proposing a joint optimization framework that efficiently solves it using an inexact Augmented Lagrangian Method, with simulation results confirming robust and high-resolution estimates.
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.