DOA Estimation for Sparse Array via Sparse Signal Reconstruction

Hu, Nan; Ye, Zhongfu; Xu, Xu; Bao, Ming

doi:10.1109/taes.2013.6494379

Cited by 102 publications

(44 citation statements)

References 29 publications

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“…These algorithms include the maximum likelihood algorithm [9], the iterative autocalibration method [10], the auxiliary sensor-based methods [11][12][13][14], the cumulant-based method [15], the Rankreduction (RARE)-based calibration methods [16,17], the sparse representation-based methods [18][19][20], and the matrix reconstruction method [21]. However, some of these methods require a set of calibration signals/auxiliary sensors [9,[11][12][13][14] or iterative/high order statistics/nonlinear optimization computations [10,[15][16][17][18][19][20]. Moreover, all such methods are designed for scalar sensor arrays and are not applicable to the vector sensor arrays.…”

Section: Introductionmentioning

confidence: 99%

Direction finding with a single spatially stretched vector sensor in the presence of mutual coupling

Shu

Wang

et al. 2018

EURASIP J. Adv. Signal Process.

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This paper is concerned with DOA estimation using a single-electromagnetic vector sensor in the presence of mutual coupling. Firstly, we apply the temporally smoothing technique to improve the identifiability limit of a single-vector sensor. In particular, we establish sufficient conditions for constructing temporally smoothed matrices to resolve K > 2 incompletely polarized (IP) monochromatic signals with a single-vector sensor. Then, we propose an efficient ESPRIT-based method, which does not require any calibration signals or iterative operations, to jointly estimate the azimuth-elevation angles and the mutual coupling coefficients. Finally, we derive the Cramér-Rao bound (CRB) for the problem under consideration.

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Section: Introductionmentioning

confidence: 99%

Direction finding with a single spatially stretched vector sensor in the presence of mutual coupling

Shu

Wang

et al. 2018

EURASIP J. Adv. Signal Process.

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“…The limit of K-R MUSIC is that the number of sources is required in advance and the expected estimation accuracy is barely achieved. The other solution proposed in [9] is called K-R CS method, in which compressed sensing (CS) theory is used to avoid the influence caused by coherence and DOA angle estimation is obtained without knowing the number of sources beforehand. Whereas, false peaks make a bad influence on K-R CS and even let it fail in a low SNR condition.…”

Section: Introductionmentioning

confidence: 99%

High-resolution direction-of-arrival estimation using Khatri-Rao product and spatial sparsity of sources

Ding¹,

Tong²,

Ge³

2016

Proceedings of the 2016 International Symposium on Advances in Electrical, Electronics and Computer Engineering

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Abstract. Algorithms based on Khatri-Rao (K-R) product improve the degree of freedom (DOF) of virtual arrays and thus more source angles can be estimated. However, the estimation accuracy is not satisfying. In this paper, a method considering spatial sparsity (SS) of sources in K-R subspace is proposed to estimate direction-of-arrival (DOA) angle. The spurious signal of the virtual array is obtained in K-R subspace and then the DOA angle estimation is finally equal to the solution of a convex optimization problem. In the proposed K-R SS method, high estimation accuracy is achieved by taking advantage of the cross-covariance matrix information. Simulation results demonstrate the effectiveness of the proposed method.

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“…The authors of [14] increase the DOFs with the minimum redundancy arrays [15] and constructing an augmented covariance matrix. Nonuniform and sparse array arrangements are also widely employed [16][17][18][19][20][21]. Solutions based on genetic algorithm [22,23], random spacing [24], linear programming [25], and compressive sensing [26][27][28] have been proposed for phased-array thinning.…”

Section: Introductionmentioning

confidence: 99%

Joint Phased-MIMO and Nested-Array Beamforming for Increased Degrees-of-Freedom

Zhu

Chen

Shao

2015

International Journal of Antennas and Propagation

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Phased-multiple-input multiple-output (phased-MIMO) enjoys the advantages of MIMO virtual array and phased-array directional gain, but it gets the directional gain at a cost of reduced degrees-of-freedom (DOFs). To compensate the DOF loss, this paper proposes a joint phased-array and nested-array beamforming based on the difference coarray processing and spatial smoothing. The essence is to use a nested-array in the receiver and then fully exploit the second order statistic of the received data. In doing so, the array system offers more DOFs which means more sources can be resolved. The direction-of-arrival (DOA) estimation performance of the proposed method is evaluated by examining the root-mean-square error. Simulation results show the proposed method has significant superiorities to the existing phased-MIMO.

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DOA Estimation for Sparse Array via Sparse Signal Reconstruction

Cited by 102 publications

References 29 publications

Direction finding with a single spatially stretched vector sensor in the presence of mutual coupling

Direction finding with a single spatially stretched vector sensor in the presence of mutual coupling

High-resolution direction-of-arrival estimation using Khatri-Rao product and spatial sparsity of sources

Joint Phased-MIMO and Nested-Array Beamforming for Increased Degrees-of-Freedom

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