A Low-Complexity Method for Two-Dimensional Direction-of-Arrival Estimation Using an L-Shaped Array

Wang, Qing; Yang, Hang; Chen, Hua; Dong, Yang‐Yang; Wang, Laihua

doi:10.3390/s17010190

Cited by 13 publications

(15 citation statements)

References 28 publications

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“…In the second experiment, we examine the DOA estimation performance of the proposed algorithm versus the SNR in terms of the root mean square error (RMSE). We also compare the proposed algorithm with the CRB, and the JSVD [24], CCMs-PM [29], SSJD algorithms [39]. The JSVD and CCMs-PM algorithms are performed on L-shaped uniform arrays, whose sensor spacing is λ/2.…”

Section: Simulation Results and Performance Analysismentioning

confidence: 99%

“…By defining the search step of 0.1 degree, the computational complexity of JSVD is about Ofalse[M2L+8M3+1800M2false]. On the other hand, the method of cross-correlation matrices propagator method (CCMs-PM) [29], which obtain the DOAs without EVD or SVD, cost approximately Ofalse[M2L+2k3+false(7M−4false)k2+kfalse(M−1false)false(2M−kfalse)false]. The methods of JSVD and CCMs-PM are applied on L-shaped uniform arrays.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Compared with 1-D DOA estimation, the study of 2-D DOA estimation, which can obtain the azimuth and elevation angles simultaneously, has attracted much attention. Numerous high-resolution 2-D DOA estimation methods with different structured arrays have been proposed, including the two-parallel arrays [14,15], circular arrays [16,17], uniform rectangular arrays [18,19,20], and L-shaped arrays [21,22,23,24,25,26,27,28,29,30]. Due to the wider coverage area and lower Cram’er-Rao boundary (CRB) [31], great efforts have been focused on 2-D DOA estimation for L-shaped arrays.…”

Section: Introductionmentioning

confidence: 99%

“…Pair-matching is achieved by using the conjugate property of the non-zero eigenvalues of two matrices. To reduce the complexity of 2-D DOA estimation, a novel auto-matched PM for L-shaped array has been presented [29]. Unfortunately, the signal subspace is obtained directly from CCM, and thus the performance of the method is affected by the residual correlations of noise vectors.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Improved Two-Dimensional Direction-Of-Arrival Estimation Algorithm for L-Shaped Nested Arrays with Small Sample Sizes

Gao

Hao

et al. 2019

Sensors

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In this paper, an improved two-dimensional (2-D) direction of arrival (DOA) estimation algorithm for L-shaped nested arrays is proposed. Unlike the approach for a classical nested array, which use the auto-correlation matrix (ACM) to increase the degrees of freedom (DOF), we utilize the cross-correlation matrix (CCM) of different sub-arrays to generate two long consecutive virtual arrays. These acquire a large number of DOF without redundant elements and eliminate noise effects. Furthermore, we reconstruct the CCM based on the singular value decomposition (SVD) operation in order to reduce the perturbation of noise for small numbers of samples. To cope with the matrix rank deficiency of the virtual arrays, we construct the full rank equivalent covariance matrices by using the output and its conjugate vector of virtual arrays. The unitary estimation of signal parameters via rotational invariance technique (ESPRIT) is then performed on the covariance matrices to obtain the DOA of incident signals with low computational complexity. Finally, angle pairing is achieved by deriving the equivalent signal vector of the virtual arrays using the estimated angles. Numerical simulation results show that the proposed algorithm not only provides more accurate 2-D DOA estimation performance with low complexity, but also achieves angle estimation for small numbers of samples compared to existing similar methods.

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Section: Simulation Results and Performance Analysismentioning

confidence: 99%

Section: The Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Two-Dimensional Direction-Of-Arrival Estimation Algorithm for L-Shaped Nested Arrays with Small Sample Sizes

Gao

Hao

et al. 2019

Sensors

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show abstract

“…In order to reduce the computational complexity of 2-D DOA estimation, many scholars have proposed a series of methods based on L-shaped array [19]- [22] and parallel linear array [23], [24]. The complexity reduction effect is achieved by transforming the 2-D DOA estimation problem into a set of 1-D angle estimation problems.…”

Section: Introductionmentioning

confidence: 99%

Multiple Matrix Reconstruction for Two-Dimensional Direction Estimation of Mixed Signals Without Eigen Decomposition

Wang

Qiao

et al. 2020

IEEE Access

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In this paper, a novel method based on multiple matrix reconstruction without eigen decomposition is proposed for solving the problem of two-dimensional (2-D) direction-of-arrival (DOA) estimation of mixed signals impinging on a planar array composing of two parallel uniform linear arrays (ULAs). Utilizing the correlation information between the array elements of two ULAs, four novel virtual covariance matrices are achieved for avoiding the interference from additive Gaussian white noise (AWGN). To eliminate the coherence between incident signals and improve the estimation accuracy, these four matrices and their backward versions are reassembled to obtain a new joint matrix. The new joint matrix is constructed again, so that we can calculate the suitable propagator for estimating the one-dimensional (1-D) angle only by a series of linear operations. Apart from this, parting the same joint matrix, we also can directly obtain a new propagator and extend it into a virtual orthogonal space, and further estimate the 1-D angle by the subspacebased method in this paper. After we obtain the 1-D estimation outcome, a novel union matrix is constructed for the estimation of 2-D angle with correct pair-matching. We derive the Cramer-Rao bound (CRB) under the signal model assumptions and array conditions in this paper. The performance is demonstrated, and the simulation results indicate that the proposed method can distinguish 2-D mixed signals with efficiency computational complexity and high estimation accuracy. INDEX TERMS Two-dimensional (2-D) direction-of-arrival (DOA) estimation, Cramer-Rao bound (CRB), mixed signals, matrix reconstruction, propagator.

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Combined real-valued subspace based two dimensional angle estimation using L-shaped array

Zhang

2018

Digital Signal Processing

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A Low-Complexity Method for Two-Dimensional Direction-of-Arrival Estimation Using an L-Shaped Array

Cited by 13 publications

References 28 publications

An Improved Two-Dimensional Direction-Of-Arrival Estimation Algorithm for L-Shaped Nested Arrays with Small Sample Sizes

An Improved Two-Dimensional Direction-Of-Arrival Estimation Algorithm for L-Shaped Nested Arrays with Small Sample Sizes

Multiple Matrix Reconstruction for Two-Dimensional Direction Estimation of Mixed Signals Without Eigen Decomposition

Combined real-valued subspace based two dimensional angle estimation using L-shaped array

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