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Improved two-dimensional DOA estimation using parallel coprime arrays
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DOI：10.1016/j.sigpro.2019.107428.png)
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Abstract
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The conventional coprime array consists of two uniform linear subarrays to construct an effective difference coarray with desirable characteristics. Such linear coprime arrays only provide one-dimensional (1-D) direction-of-arrival (DOA) estimation. In this paper, we propose a novel coprime array configuration with parallel subarrays, along with an effective method for two-dimensional (2-D) DOA estimation. The 2-D DOA estimation problem is cast as two separate 1-D problems for reduced complexity and is solved using one of the two mechanisms based on the number of sensors and that of sources. When there are less sources than the number of sensors, subspace-based and rank-reduction estimation (RARE) techniques are sequentially applied to the physical array output. On the other hand, when the number of sources is equal to or larger than that of sensors, a virtual difference coarray is formed and group sparse reconstruction and least squares operations are then applied. In both scenarios, the proposed methods automatically pair the corresponding azimuth and elevation angles. The proposed methods resolve up to MN sources using 2M N + 1 sensors, which are the same as in the 1-D DOA estimation using conventional coprime arrays. Simulations results are presented delineating both the accuracy and resolution capability of the proposed method. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Two-dimensional direction-of-arrival estimation
Coprime array
Sparse array
Parallel subarray
Compressive sensing
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