Abstract
For two-dimensional (2D) direction of arrival (DOA) estimation in a uniform rectangular array (URA), the conventional method converts the 2D problem into a one-dimensional (1D) problem; however, the computational complexity is high, and the accuracy is limited by the grid interval. To address this issue, based on sparse representation theory and a separable observation model (SPM), this paper presents a novel off-grid-framework-based 2D DOA estimation approach by designing a modified 2D off-grid model and a solution for the multisnapshot case in the SPM. The proposed algorithm can be divided into two stages. In the first stage, we use a matching pursuit and focal underdetermined system solver (MFOCUSS) algorithm to quickly identify the candidate or potential areas where the true sources may exist. In the second stage, the candidate areas obtained in the first stage are regarded as the initialization. Then, for a specific source, we regard other sources as interference. By using an alternating descent method, we can obtain accurate DOAs. Moreover, based on the equivalence of time delay and spatial spacing, a 2D off-grid method for multisnapshot cases is proposed in this paper. Numerical simulations demonstrate the effectiveness and efficiency of the proposed algorithm.
Highlights
Source localization using sensor arrays has been an active research field for decades
This paper focuses on the far-field narrowband signal case where the wavefront is assumed to be planar and the direction information is estimated, known as a direction of arrival (DOA) estimation problem
A great number of DOA estimation algorithms based on sparse signal reconstruction (SSR) or compressive sensing (CS) have been developed, such as [2], The associate editor coordinating the review of this article and approving it for publication was Bora Onat
Summary
Source localization using sensor arrays has been an active research field for decades. Compared with conventional methods, such as beam-forming [8] and MUSIC, these CS-based algorithms have superior performance, including increased resolution, better robustness to noise, and the ability to work with a limited number of snapshots and even one snapshot Though these CS-based methods have shown their improvement in DOA estimation, they still suffer from one common problem in situations where the true DOAs are not on the sampling grid. This method applies the least squares method to estimate biases and uses an alternating descent method to solve the joint optimization problem This algorithm requires that the azimuths and elevations of different sources are in different grids. We propose a novel CS-based 2D off-grid DOA estimation algorithm for a URA. To estimate the DOAs {(φk , θk )}Kk=1, we need to use the received array data y and information on the array geometry
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