Study of Robust Direction Finding Based on Joint Sparse Representation

TL;DR

This paper introduces a robust direction finding method that leverages joint sparse representation to effectively estimate directions of arrival in impulsive noise environments, outperforming traditional Gaussian-based methods.

Contribution

It proposes a novel DOA estimation technique based on sparse signal recovery that is robust to impulsive noise and addresses grid mismatch through an alternating optimization approach.

Findings

Demonstrates robustness against large outliers in simulations

Outperforms traditional Gaussian noise-based DOA methods

Effectively estimates off-grid DOAs with improved accuracy

Abstract

Standard Direction of Arrival (DOA) estimation methods are typically derived based on the Gaussian noise assumption, making them highly sensitive to outliers. Therefore, in the presence of impulsive noise, the performance of these methods may significantly deteriorate. In this paper, we model impulsive noise as Gaussian noise mixed with sparse outliers. By exploiting their statistical differences, we propose a novel DOA estimation method based on sparse signal recovery (SSR). Furthermore, to address the issue of grid mismatch, we utilize an alternating optimization approach that relies on the estimated outlier matrix and the on-grid DOA estimates to obtain the off-grid DOA estimates. Simulation results demonstrate that the proposed method exhibits robustness against large outliers.