Alternating projections gridless covariance-based estimation for DOA

TL;DR

This paper introduces a gridless, sparse covariance-based method using alternating projections for DOA estimation, capable of resolving more sources than sensors, even with single snapshots and coherent signals.

Contribution

It proposes a novel gridless DOA estimation technique based on Toeplitz low-rank matrix reconstruction and alternating projections, enhancing resolution and handling challenging scenarios.

Findings

Improves resolution by achieving sparsity.

Handles single-snapshot and coherent arrivals.

Estimates more DOAs than sensors with co-prime arrays.

Abstract

We present a gridless sparse iterative covariance-based estimation method based on alternating projections for direction-of-arrival (DOA) estimation. The gridless DOA estimation is formulated in the reconstruction of Toeplitz-structured low rank matrix, and is solved efficiently with alternating projections. The method improves resolution by achieving sparsity, deals with single-snapshot data and coherent arrivals, and, with co-prime arrays, estimates more DOAs than the number of sensors. We evaluate the proposed method using simulation results focusing on co-prime arrays.