A novel DOA estimation method for uncorrelated and coherent signals via compressed sensing in sparse arrays

The performance of a direction-finding system is significantly degraded by the imperfection of an array. In this paper, the direction-of-arrival (DOA) estimation problem is investigated in the uniform linear array (ULA) system with the unknown mutual coupling (MC) effect. The system model with MC effect is formulated. Then, by exploiting the signal sparsity in the spatial domain, a compressed-sensing (CS)-based system model is proposed with the MC coefficients, and the problem of DOA estimation is converted into that of a sparse reconstruction. To solve the reconstruction problem efficiently, a novel DOA estimation method, named sparse-based DOA estimation with unknown MC effect (SDMC), is proposed, where both the sparse signal and the MC coefficients are estimated iteratively. Simulation results show that the proposed method can achieve better performance of DOA estimation in the scenario with MC effect than the state-of-the-art methods, and improve the DOA estimation performance about 31.64 % by reducing the MC effect by about 4 dB.

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A DOA Estimation Method With Kronecker Subspace for Coherent Signals

IEEE Communications Letters ◽

10.1109/lcomm.2018.2870824 ◽

2018 ◽

Vol 22 (11) ◽

pp. 2306-2309 ◽

Cited By ~ 3

Author(s):

Masaaki Inoue ◽

Kazunori Hayashi ◽

Hiroki Mori ◽

Toshihisa Nabetani

Keyword(s):

Estimation Method ◽

Doa Estimation ◽

Coherent Signals

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A Novel ULA-Difference-Coarray-Based DOA Estimation Method for General Coherent Signals

Mathematical Problems in Engineering ◽

10.1155/2020/1548016 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Zhang Chen ◽

Hao Wu ◽

Yongxiang Liu

Keyword(s):

Good Accuracy ◽

Linear Array ◽

Estimation Method ◽

Doa Estimation ◽

Estimation Accuracy ◽

Coherent Signals ◽

Low Snr ◽

Sample Covariance ◽

Lower Complexity ◽

The Difference

In this article, a difference-coarray-based direction of arrival (DOA) method is introduced, which utilizes the uniform linear array (ULA) in a novel fashion to address the problem of DOA estimation for coherent signals. Inspired by the coarray-based estimators employed in cases of sparse arrays, we convert the sample covariance matrix of the observed signals into the difference coarray domain and process the signals using a spatial smoothing technique. The proposed method exhibits good accuracy and robustness in both the uncorrelated and coherent cases. Numerical simulations verify that the ULA difference coarray- (UDC-) based method can achieve good DOA estimation accuracy even when the SNR is very low. In addition, the UDC-based method is insensitive to the number of snapshots. Under extremely challenging conditions, the proposed UDC-ES-DOA method is preferred because of its outstanding robustness, while the UDC-MUSIC method is suitable for most moderate cases of lower complexity. Due to its demonstrated advantages, the proposed method is a promising and competitive solution for practical DOA estimation, especially for low-SNR or snapshot-limited applications.

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DOA Estimation Method in Multipath Environment for Passive Bistatic Radar

International Journal of Antennas and Propagation ◽

10.1155/2019/7419156 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Panhe Hu ◽

Qinglong Bao ◽

Zengping Chen

Keyword(s):

Signal To Noise Ratio ◽

Estimation Method ◽

Doa Estimation ◽

Estimation Accuracy ◽

Bistatic Radar ◽

Spatial Difference ◽

Coherent Signals ◽

Vhf Radar ◽

Multipath Environment ◽

Passive Bistatic Radar

Direction-of-arrival (DOA) estimation in multipath environment is an important issue for passive bistatic radar (PBR) using frequency agile phased array VHF radar as illuminator of opportunity. Under such scenario, the main focus of this paper is to cope with the closely spaced uncorrelated and coherent signals in low signal-to-noise ratio and limited snapshots. Making full use of the characteristics of moduli of eigenvalues, the DOAs of the uncorrelated signals are firstly estimated. Afterwards, their contributions are eliminated by means of spatial difference technique. Finally, in order to improve resolution and accuracy DOA estimation of remaining coherent signals while avoiding the cross-terms effect, a new beamforming solution based iterative adaptive approach (IAA) is proposed to deal with a reconstructed covariance matrix. The proposed method combines the advantages of both spatial difference method and the IAA algorithm while avoiding their shortcomings. Simulation results validate its effectiveness; meanwhile, the good performances of the proposed method in terms of resolution probability, detection probability, and estimation accuracy are demonstrated by comparison with the existing methods.

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An effective DOA estimation method of coherent signals based on reconstruct weighted noise subspace

2017 29th Chinese Control And Decision Conference (CCDC) ◽

10.1109/ccdc.2017.7978883 ◽

2017 ◽

Author(s):

Liu Xiaozhi ◽

Song Muye ◽

Yang Yinghua

Keyword(s):

Estimation Method ◽

Doa Estimation ◽

Coherent Signals ◽

Noise Subspace

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Multiple Sparse Measurement Gradient Reconstruction Algorithm for DOA Estimation in Compressed Sensing

Mathematical Problems in Engineering ◽

10.1155/2015/152570 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Weijian Si ◽

Xinggen Qu ◽

Yilin Jiang ◽

Tao Chen

Keyword(s):

Convex Optimization ◽

Compressed Sensing ◽

Signal To Noise Ratio ◽

Estimation Method ◽

Reconstruction Algorithm ◽

Doa Estimation ◽

Superior Performance ◽

Multiple Measurement Vectors ◽

Gradient Reconstruction ◽

Quadratically Constrained

A novel direction of arrival (DOA) estimation method in compressed sensing (CS) is proposed, in which the DOA estimation problem is cast as the joint sparse reconstruction from multiple measurement vectors (MMV). The proposed method is derived through transforming quadratically constrained linear programming (QCLP) into unconstrained convex optimization which overcomes the drawback thatl1-norm is nondifferentiable when sparse sources are reconstructed by minimizingl1-norm. The convergence rate and estimation performance of the proposed method can be significantly improved, since the steepest descent step and Barzilai-Borwein step are alternately used as the search step in the unconstrained convex optimization. The proposed method can obtain satisfactory performance especially in these scenarios with low signal to noise ratio (SNR), small number of snapshots, or coherent sources. Simulation results show the superior performance of the proposed method as compared with existing methods.

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New DOA Estimation Method for Wideband Coherent Signals in the Presence of Correlated Noise

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.157 ◽

2012 ◽

Vol 263-266 ◽

pp. 157-161 ◽

Cited By ~ 1

Author(s):

Jin Zhang ◽

Yun Xiang Mao ◽

Jian Yun Zhang

Keyword(s):

Numerical Simulation ◽

Antenna Array ◽

Covariance Matrix ◽

Estimation Method ◽

Direction Of Arrival ◽

Doa Estimation ◽

Correlated Noise ◽

Linear Antenna ◽

Coherent Signals ◽

Linear Antenna Array

With a uniform linear antenna array, a new direction-of-arrival (DOA) estimation method is proposed for wideband coherent signals in the presence of unknown correlated noise but with structured covariance matrix. Based on this proposed structure, i.e. Hermitian Toeplitz, a spatial differencing operation that exploits this symmetry is applied to remove the effect of the unknown noise and a new matrix is constructed accordingly at each frequency bin. Following this step, a focusing operation is performed to give the corresponding aligned covariance matrix. Finally, an eigenstructure-based DOA estimation method is applied. The validity of the method is supported by numerical simulation under various conditions.

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The Analysis of Using Spatial Smoothing for DOA Estimation of Coherent Signals in Sparse Arrays

International Journal of Antennas and Propagation ◽

10.1155/2021/4447562 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Haiyun Xu ◽

Weijia Cui ◽

Yuxi Du ◽

Fengtong Mei ◽

Bin Ba

Keyword(s):

Numerical Experiments ◽

Direction Of Arrival ◽

Doa Estimation ◽

The Other ◽

Feasibility Analysis ◽

Spatial Smoothing ◽

Coherent Signals ◽

Sparse Arrays

When there is coexistence of uncorrelated and coherent signals in sparse arrays, the conventional algorithms for direction-of-arrival (DOA) estimation using difference coarray fail. In order to solve the problems, this paper analyzes the feasibility of using spatial smoothing in sparse arrays. Firstly, we summarize the two types of sparse arrays, one consisting of identical sparse subarrays and the other consisting of several uniform linear subarrays. Then, we give the feasibility analysis and the processes of applying spatial smoothing. At last, we discuss the performance of the number of detectable coherent signals in different sparse arrays. Numerical experiments prove the conclusions proposed by the paper.

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Tensor Approach to DOA Estimation of Coherent Signals with Electromagnetic Vector-Sensor Array

Sensors ◽

10.3390/s18124320 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4320 ◽

Cited By ~ 2

Author(s):

Ming-Yang Cao ◽

Xingpeng Mao ◽

Xiaozhuan Long ◽

Lei Huang

Keyword(s):

Estimation Method ◽

Doa Estimation ◽

Parameters Estimation ◽

Estimation Accuracy ◽

Coherent Signals ◽

Rectangular Array ◽

Vector Sensor ◽

Electromagnetic Vector Sensor ◽

Vector Sensors ◽

Uniform Rectangular Array

This paper addresses the direction-of-arrival (DOA) estimation problem using a uniform rectangular array with electromagnetic vector-sensors in correlated/coherent signal environments. The polarization information is separated from the steering matrix to decorrelate the signals. By developing a tensorial structured received measurements of the array, we propose a tensor-based eigenvector DOA estimation method. Then we apply the forward-backward averaging to the tensor since it obeys the centro-Hermitian structure. In addition, a tensor-based polarization parameters estimation method is presented. The proposed algorithms are superior to the state-of-the-art algorithms in terms of estimation accuracy of coherent signals while only demand a modest computation burden comparing with the latter ones. Simulation results are given to demonstrate the effectiveness of the proposed methods under different scenarios.

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