Sparsity-Based Off-Grid DOA Estimation With Uniform Rectangular Arrays

Abstract

Conventional sparsity-based direction of arrival (DOA) estimation algorithms suffer from performance degradation due to the off-grid problem. To address it, several research studies focused on 1-D off-grid problem have been conducted. However, due to the bias coupling between the azimuth and elevation directions, those works cannot be directly extended to the uniform rectangular array. In this paper, we propose an effective sparsity-based 2-D DOA estimation algorithm. The proposed algorithm provides the following benefits: 1) we introduce a more accurate observation model where the bias coupling problem is eliminated and each grid bias can be compensated individually and 2) besides the sparsity constraint, we engage additional energy constraint over the biases to improve the estimation performance. Due to the presence of multiple unknown parameters, an alternating descent method is adopted to solve the associated joint optimization problem. Finally, simulational results demonstrate that the proposed algorithm, compared with competitors, exhibits superior performance including high precision DOA estimation and low computational complexity.