Unfolded coprime L-shaped arrays for two-dimensional direction of arrival estimation

Gong, Pan; Zhang, Xiaofei; Wang, Zheng

doi:10.1080/00207217.2018.1460874

Cited by 12 publications

(8 citation statements)

References 30 publications

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“…This section will calculate and compare the computational complexity of RD-MUSIC and root-RD-MUSIC proposed in this paper with the known RC-MUSIC [21] and 2D-MUSIC algorithm under Lshaped uniform array according to the previous analysis and [11,13,21].…”

Section: Complexity Analysismentioning

confidence: 99%

“…Early research on these algorithms mostly stayed in one-dimensional (1D) array. Since 1D DOA estimation can only provide 1D estimation information of incident wave, there are many defects in practical application, so more and more researchers are engaged in the research of two-dimensional (2D) DOA estimation [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Among these research results, the 2D-MUSIC algorithm is one of the most classic algorithms in current 2D-DOA estimation research.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [21] proposed a reduced complexity MUSIC (RC-MUSIC) algorithm based on unfolding coprime L-shaped array. This algorithm uses the cross-correlation information of the received signals between two subarrays to convert the 2D peak search into 1D search, which reduces the complexity of 2D-MUSIC algorithm, increases the array aperture, and improves the estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

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A 2-D Doa Estimation Algorithm for L-Shaped Array With Improved Computational Efficiency

Yang¹,

He²

2022

PIER M

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A high-precision and high-efficiency reduced-dimension direction of arrival (DOA) estimation algorithm based on an L-shaped array for the problems of large computation and high cost of achieving two-dimensional (2D) DOA estimation by 2D multiple signal classification (MUSIC) algorithm under various complex arrays. The algorithm makes full use of the structural characteristics of the L-shaped array to decompose the uniform L-shaped array into two uniform linear arrays. These two arrays are respectively searched in one-dimension (1D) to estimate the angles between the source and the x-axis and y-axis, and then the 2D DOA estimation is obtained according to the geometric relationship, which greatly reduces the amount of computation. Furthermore, the algorithm increases the utilization of noise subspace information, which not only realizes the automatic pairing of direction angle and elevation angle, but also improves the estimation accuracy. In order to further reduce the complexity and improve the estimation performance, this paper also puts forward the root finding method instead of 1D search and uses a fast angle matching method to accurately match angles. Simulation results show the feasibility of the proposed algorithm.

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Section: Complexity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A 2-D Doa Estimation Algorithm for L-Shaped Array With Improved Computational Efficiency

Yang¹,

He²

2022

PIER M

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“…In recent years, underdetermined DOA estimation, where the number of sources is larger than that of array elements, has attracted increasing attention. With the development of sparse array, such as nested array [2–5] and coprime array [6–8], the underdetermined problem can be solved effectively. The crucial idea is to construct the difference co‐array by vectorising the covariance matrix of the array outputs, which results in an increase of the degree of freedom (DOF) [9].…”

Section: Introductionmentioning

confidence: 99%

Underdetermined wideband DOA estimation for off‐grid targets: a computationally efficient sparse Bayesian learning approach

Jiang

Liu

et al. 2020

IET Radar, Sonar & Navigation

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“…Furthermore, instead of treating the two subarrays separately, MUSIC is performed with the received signal of the whole array. This technique, having been extended to the scenarios of noncircular signals in [7] and two-dimensional DOA estimation in [8], enjoys the advantages that both self and mutual information can be explored and full DOFs can be obtained. Besides, it has been admitted that the ambiguity problem can be suppressed thanks to the co-prime property.…”

Section: Introductionmentioning

confidence: 99%

Modified DOA Estimation With an Unfolded Co-Prime Linear Array

2019

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Direction-of-arrival estimation with unfolded coprime linear arrays has been investigated a lot thanks to the large aperture, excellent performance and full degrees-of-freedom (DOF). Besides, it has been admitted that the ambiguity problem can be suppressed thanks to the co-prime property. However, it is not always true. When there are two different DOAs having the same directional vectors with a given DOA for the two subarrays respectively, ambiguities would still be found at the angles of which the corresponding directional vectors can be represented as linear combinations of the directional vectors of true DOAs. Surprisingly, this problem has been ignored by all the existing work. In this letter, this ambiguity problem is discussed for the first time to our best knowledge, and an appropriate method for fixing it is proposed. Compared to the original method, the proposed method contains all the advantages of unfolded co-prime linear arrays, and can solve the ambiguity problem successfully and efficiently. Numerical simulations are given to show the reliability and performance of the proposed method.

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Unfolded coprime L-shaped arrays for two-dimensional direction of arrival estimation

Cited by 12 publications

References 30 publications

A 2-D Doa Estimation Algorithm for L-Shaped Array With Improved Computational Efficiency

A 2-D Doa Estimation Algorithm for L-Shaped Array With Improved Computational Efficiency

Underdetermined wideband DOA estimation for off‐grid targets: a computationally efficient sparse Bayesian learning approach

Modified DOA Estimation With an Unfolded Co-Prime Linear Array

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