Off-Grid Direction-of-Arrival Estimation Using Second-Order Taylor Approximation

TL;DR

This paper introduces a novel off-grid DOA estimation method using second-order Taylor approximation within a joint-sparse framework, improving accuracy over existing grid-based techniques.

Contribution

The paper develops a second-order Taylor approximation-based grid method for off-grid DOA estimation, addressing grid mismatch issues in a joint-sparse model.

Findings

Method outperforms state-of-the-art grid-based approaches in simulations.

Proportionality property of signals enhances the mixing matrix's restricted isometry.

Simulation results confirm the effectiveness and superiority of the proposed approach.

Abstract

The problem of off-grid direction-of-arrival (DOA) estimation is investigated. We develop a grid-based method to jointly estimate the closest spatial frequency (the sine of DOA) grids, and the gaps between the estimated grids and the corresponding frequencies. By using a second-order Taylor approximation, the data model under the framework of joint-sparse representation is formulated. We point out an important property of the signals of interest in the model, namely the proportionality relationship, which is empirically demonstrated to be useful in the sense that it increases the probability of the mixing matrix satisfying the block restricted isometry property. Simulation examples demonstrate the effectiveness and superiority of the proposed method against several state-of-the-art grid-based approaches.