Two-snapshot DOA Estimation via Hankel-structured Matrix Completion

TL;DR

This paper introduces a novel approach for DOA estimation using sparse sampling and Hankel-structured matrix completion, enabling accurate source localization with fewer array elements and outperforming existing methods.

Contribution

The paper proposes a new sparse sampling strategy combined with Hankel-structured matrix completion for DOA estimation, providing theoretical recovery bounds and demonstrating superior performance.

Findings

The method achieves perfect recovery with fewer samples than traditional techniques.

Numerical results show improved accuracy over atomic-norm minimization.

Theoretical bounds guarantee the number of samples needed for successful recovery.

Abstract

In this paper, we study the problem of estimating the direction of arrival (DOA) using a sparsely sampled uniform linear array (ULA). Based on an initial incomplete ULA measurement, our strategy is to choose a sparse subset of array elements for measuring the next snapshot. Then, we use a Hankel-structured matrix completion to interpolate for the missing ULA measurements. Finally, the source DOAs are estimated using a subspace method such as Prony on the fully recovered ULA. We theoretically provide a sufficient bound for the number of required samples (array elements) for perfect recovery. The numerical comparisons of the proposed method with existing techniques such as atomic-norm minimization and off-the-grid approaches confirm the superiority of the proposed method.