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Sensors 2018, 18(6), 1861; https://doi.org/10.3390/s18061861

Two-Dimensional DOA Estimation for Three-Parallel Nested Subarrays via Sparse Representation

College of Information and Communication Engineering, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China
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Received: 7 May 2018 / Revised: 25 May 2018 / Accepted: 3 June 2018 / Published: 7 June 2018
(This article belongs to the Section Physical Sensors)
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Abstract

Nested arrays are considered attractive due to their hole-free performance, and have the ability to resolve O ( N 2 ) sources with O ( N ) physical sensors. Inspired by nested arrays, two kinds of three-parallel nested subarrays (TPNAs), which are composed of three parallel sparse linear subarrays with different inter-element spacings, are proposed for two-dimensional (2-D) direction-of-arrival (DOA) estimation in this paper. We construct two cross-correlation matrices and combine them as one augmented matrix in the first step. Then, by vectorizing the augmented matrix, a hole-free difference coarray with larger degrees of freedom (DOFs) is achieved. Meanwhile, sparse representation and the total least squares technique are presented to transform the problem of 2-D DOA searching into 1-D searching. Accordingly, we can obtain the paired 2-D angles automatically and improve the 2-D DOA estimation performance. In addition, we derive closed form expressions of sensor positions, as well as number of sensors for different subarrays of two kinds of TPNA to maximize the DOFs. In the end, the simulation results verify the superiority of the proposed TPNAs and 2-D DOA estimation method. View Full-Text
Keywords: two-dimensional DOA estimation; three-parallel nested subarrays; sparse representation; cross-correlation matrix; degrees of freedom two-dimensional DOA estimation; three-parallel nested subarrays; sparse representation; cross-correlation matrix; degrees of freedom
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Si, W.; Peng, Z.; Hou, C.; Zeng, F. Two-Dimensional DOA Estimation for Three-Parallel Nested Subarrays via Sparse Representation. Sensors 2018, 18, 1861.

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