DOA Estimation for Coprime Linear Arrays: An Ambiguity-Free Method Involving Full DOFs

Wang, Zheng; Zhang, Xiaofei; Gong, Pan; Zhai, Huiqing

doi:10.1109/lcomm.2017.2787698

Cited by 72 publications

(48 citation statements)

References 15 publications

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“…The Cramér-Rao lower bound (CRB) for the unconditional model is also given as a benchmark [17]. Figure 2 depicts the RMSE performance of Zheng's method in [15] and the proposed method versus the signal-to-noise ratio (SNR) with K = 2 and L = 200, in both distantly separated angles situation (denoted as general angles in Figure 2), where signals come from {20 • , 50 • }, and a close angle situation, where signals come from {24 • , 25 • }. It can be seen that, in the distantly separated angles situation, the estimation performance is comparable to Zheng's method; in the close angles situation, due to the reconstruction of the covariance matrix and the exploitation of the uniform property of subarrays, the proposed method has higher resolution and better estimation performance than Zheng's method.…”

Section: Estimation Performancementioning

confidence: 99%

“…In order to solve these problems, a method based on a new geometry of unfolded coprime linear arrays (UCLAs) is proposed in [15]. By rotating a subarray of a CLA 180 • , a non-uniform linear array with a larger aperture can be obtained.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, instead of traditional MUSIC, root-MUSIC is used to avoid the step of peak-searching. Compared with the method in [15], the proposed method can achieve higher resolution and better estimation performance with lower computational complexity. Simulation results are provided to show the performance of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

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A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays

Yang

Wang

2019

Sensors

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The direction-of-arrivals (DOA) estimation with an unfolded coprime linear array (UCLA) has been investigated because of its large aperture and full degrees of freedom (DOFs). The existing method suffers from low resolution and high computational complexity due to the loss of the uniform property and the step of exhaustive peak searching. In this paper, an improved DOA estimation method for a UCLA is proposed. To exploit the uniform property of the subarrays, the diagonal elements of the two self-covariance matrices are averaged to enhance the accuracy of the estimated covariance matrices and therefore the estimation performance. Besides, instead of the exhaustive peak searching, the polynomial roots finding method is used to reduce the complexity. Compared with the existing method, the proposed method can achieve higher resolution and better estimation performance with lower computational complexity.

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Section: Estimation Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays

Yang

Wang

2019

Sensors

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“…By exploiting the coprime property, the phase ambiguity problem is tackled. In [25], an ambiguity-free DOA estimation algorithm was proposed, which exploits the total information including self-information and mutual information to eliminate the ambiguity problem with high accuracy of DOA estimates. However, due to the total angular search, this algorithm results in large computational complexity.…”

Section: Introductionmentioning

confidence: 99%

“…To tackle the problem, an improved DOA estimation algorithm based on root-MUSIC was proposed in [27] for coprime linear array (CLA), which employs the relation between steering matrices and signal subspaces of two subarrays to achieve DOA estimation. e algorithms mentioned above [24][25][26][27] were presented for one-dimensional (1D) DOA estimation, whereas, practically, two-dimensional (2D) DOA estimation possesses more importance, and various studies have been introduced [28][29][30]. In [28], the PSS algorithm for coprime planar array (CPA) was presented, which can reduce the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Coprime Array for Massive MIMO: Array Configuration Design and 2D DOA Estimation

Gong

Ahmed

2020

Wireless Communications and Mobile Computing

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In massive multiple-input multiple-output (MIMO) systems, it is critical to obtain the accurate direction of arrival (DOA) estimation. Conventional three-dimensional array mainly focuses on the uniform array. Due to the dense arrangement of the sensors, the array aperture is limited and severe mutual coupling effects arise. In this paper, a coprime cubic array (CCA) configuration design is presented, which is composed of two uniform cubic subarrays and can extend the interelement spacing with a selection of three pairs of coprime integers. Compared with uniform cubic array (UCA), CCA achieves the larger array aperture and less MC effects. And the analytical expression of Cramer–Rao Bound (CRB) for CCA is derived which verifies that the proposed CCA geometry outperforms the conventional UCA in two-dimensional (2D) DOA estimation performance in massive MIMO systems. Meanwhile, we propose a computationally efficient 2D DOA estimation algorithm with high accuracy for CCA. Specifically, we utilize array mapping to extract two uniform arrays from the nonuniform array by exploiting the relation derived from the signal subspace and the two directional matrices. Then, we operate a reduced dimension process on the uniform arrays and convert the 2D spectrum peak searching (SPS) problem into one-dimensional (1D) one, which significantly reduces the computational complexity. In addition, we employ the polynomial root finding technique with a lower complexity instead of 1D SPS to further relieve the computational complexity. Simultaneously, with coprime property, the phase ambiguity problem is solved, which results from the large interelement spacing. Numerical simulation results demonstrate that the proposed algorithm is very computationally efficient without degradation of DOA estimation performance.

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An Efficient Method for Resolving Ambiguity in DOA Estimation with Coprime Linear Array

Ashok

Venkateswaran²

2021

Circuits Syst Signal Process

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DOA Estimation for Coprime Linear Arrays: An Ambiguity-Free Method Involving Full DOFs

Cited by 72 publications

References 15 publications

A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays

A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays

Three-Dimensional Coprime Array for Massive MIMO: Array Configuration Design and 2D DOA Estimation

An Efficient Method for Resolving Ambiguity in DOA Estimation with Coprime Linear Array

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