An Efficient 2D DOA Estimation Algorithm Based on OMP for Rectangular Array

Wang, Chuang; Hu, Jianmin; Zhang, Qunying; Yuan, Xinhao

doi:10.3390/electronics12071634

Cited by 2 publications

(2 citation statements)

References 35 publications

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“…Conversely, the sparse reconstruction algorithms are intended to build mathematical models between array observation data and the 2D DOA, followed by a series of optimization steps based on different matching criteria, which no longer require the number of sources as a prior and are robust to noise. Nevertheless, the original sparsity-based algorithms divide the entire spatial directions into discrete grids [12][13][14], forming a redundant dictionary to formulate the array data, where the grid mismatch problem may occur to a large extent. In view of this, varieties of off-grid algorithms have been proposed, one after the other, to overcome this strict grid limit by introducing quantization errors between divided grids and real values [15][16][17], and many types of strategies are applied to approximate these errors instead.…”

Section: Introductionmentioning

confidence: 99%

Efficient 2D DOA Estimation via Decoupled Projected Atomic Norm Minimization

Liu,

Dong,

Dong

et al. 2024

Electronics

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This paper presents an efficient two-dimensional (2D) direction of arrival (DOA) estimation method, termed as decoupled projected atomic norm minimization (D-PANM), to solve the angle-ambiguity problem. It first introduces a novel atomic metric via projecting the original atom set onto a smoothing space, based on which we formulate an equivalent semi-definite programming (SDP) problem. Then, two relatively low-complexity decoupled Toeplitz matrices can be obtained to estimate the DOAs. We further exploit the structural information hidden in the newly constructed data to avoid pair matching for the azimuth and elevation angles when the number of sensors is odd, and then propose a fast and feasible decoupled alternating projections (D-AP) algorithm, reducing computational complexity to a great extent. Numerical simulations are performed to demonstrate that the proposed algorithm is no longer restricted by angle ambiguity scenarios, but instead provides a more stable estimation performance, even when multiple signals share the same angles in both azimuth and elevation dimensions. Additionally, it greatly improves the resolution, with control of the computation load compared with the existing atomic norm minimization (ANM) algorithm.

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Section: Introductionmentioning

confidence: 99%

Efficient 2D DOA Estimation via Decoupled Projected Atomic Norm Minimization

Liu,

Dong,

Dong

et al. 2024

Electronics

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show abstract

“…Now, the 2-D direction of arrival estimation problem is widely used in radar, internet of vehicle (IOV) and the fifth-generation (5 G) mobile communications [ 1 , 2 , 3 , 4 , 5 , 6 ]. And many algorithms have been developed to solve the problem of DOA estimation, such as improved reduced dimension MUSIC (IRD-MUSIC), the reduced-dimension multiple signal classification algorithm, and so on [ 7 , 8 , 9 , 10 , 11 , 12 ]. Compared to a 2-D planar array, L-shaped sparse arrays have lower costs and better adaptability.…”

Section: Introductionmentioning

confidence: 99%

An L-Shaped Three-Level and Single Common Element Sparse Sensor Array for 2-D DOA Estimation

Cui

et al. 2023

Sensors

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The degree of freedom (DOF) is an important performance metric for evaluating the design of a sparse array structure. Designing novel sparse arrays with higher degrees of freedom, while ensuring that the array structure can be mathematically represented, is a crucial research direction in the field of direction of arrival (DOA) estimation. In this paper, we propose a novel L-shaped sparse sensor array by adjusting the physical placement of the sensors in the sparse array. The proposed L-shaped sparse array consists of two sets of three-level and single-element sparse arrays (TSESAs), which estimate the azimuth and elevation angles, respectively, through one-dimensional (1-D) spatial spectrum search. Each TSESA is composed of a uniform linear subarray and two sparse subarrays, with one single common element in the two sparse subarrays. Compared to existing L-shaped sparse arrays, the proposed array achieves higher degrees of freedom, up to 4Q1Q2+8Q1−5, when estimating DOA using the received signal covariance. To facilitate the correct matching of azimuth and elevation angles, the cross-covariance between the two TSESA arrays is utilized for estimation. By comparing and analyzing performance parameters with commonly used L-shaped and other sparse arrays, it is found that the proposed L-shaped TSESA has higher degrees of freedom and array aperture, leading to improved two-dimensional (2-D) DOA estimation results. Finally, simulation experiments validate the excellent performance of the L-shaped TSESA in 2-D DOA estimation.

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An Efficient 2D DOA Estimation Algorithm Based on OMP for Rectangular Array

Cited by 2 publications

References 35 publications

Efficient 2D DOA Estimation via Decoupled Projected Atomic Norm Minimization

Efficient 2D DOA Estimation via Decoupled Projected Atomic Norm Minimization

An L-Shaped Three-Level and Single Common Element Sparse Sensor Array for 2-D DOA Estimation

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