Low-complexity DOA estimation method for a co-prime linear array

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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Low Complexity Sparse Beamspace DOA Estimation Via Single Measurement Vectors for Uniform Circular Array

10.21203/rs.3.rs-278792/v1 ◽

2021 ◽

Author(s):

Di Zhao ◽

Weijie Tan ◽

Zhongliang Deng ◽

Gang Li

Keyword(s):

Optimization Problem ◽

Signal To Noise Ratio ◽

Estimation Method ◽

Doa Estimation ◽

Low Complexity ◽

Estimation Methods ◽

Circular Array ◽

Single Measurement ◽

Signal Model ◽

Convex Optimization Problem

Abstract In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.

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Peak‐to‐peak search: fast and accurate DOA estimation method for arbitrary non‐uniform linear array

Electronics Letters ◽

10.1049/el.2015.1500 ◽

2015 ◽

Vol 51 (25) ◽

pp. 2078-2080 ◽

Cited By ~ 4

Author(s):

Li Wei ◽

Wei Shao ◽

Wangdong Qi ◽

Jianhua Chen

Keyword(s):

Linear Array ◽

Estimation Method ◽

Doa Estimation ◽

Uniform Linear Array ◽

Peak Search

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A low-complexity 2-D DOA estimation method for incoherently distributed sources

2016 CIE International Conference on Radar (RADAR) ◽

10.1109/radar.2016.8059248 ◽

2016 ◽

Author(s):

Qingqing Lin ◽

Ping Shuai ◽

Liangwei Huang

Keyword(s):

Estimation Method ◽

Doa Estimation ◽

Low Complexity ◽

Incoherently Distributed Sources

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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 for Mixed Uncorrelated and Coherent Sources in Multipath Environment

International Journal of Antennas and Propagation ◽

10.1155/2015/636545 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 6

Author(s):

Heping Shi ◽

Wen Leng ◽

Anguo Wang ◽

Tongfeng Guo

Keyword(s):

Linear Array ◽

Estimation Method ◽

Doa Estimation ◽

Spatial Smoothing ◽

Uniform Linear Array ◽

Performance Simulation ◽

Coherent Sources ◽

Multipath Environment ◽

Simulation Results ◽

Effectiveness And Efficiency

A novel direction-of-arrival (DOA) estimation method is proposed to cope with the scenario where a number of uncorrelated and coherent narrowband sources simultaneously impinge on the far-field of a uniform linear array (ULA). In the proposed method, the DOAs of uncorrelated sources are firstly estimated by utilizing the property of the moduli of eigenvalues of the DOA matrix. Afterwards, the contributions of uncorrelated sources and the interference of noise are eliminated completely by exploiting the improved spatial differencing technique and only the coherent components remain in the spatial differencing matrix. Finally, the remaining coherent sources can be resolved by performing the improved spatial smoothing scheme on the spatial differencing matrix. The presented method can resolve more number of sources than that of the array elements and distinguish the uncorrelated and coherent sources that come from the same direction as well as improving the estimation performance. Simulation results demonstrate the effectiveness and efficiency of the proposed method.

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A Robust Direction of Arrival Estimation Method for Uniform Circular Array

Sensors ◽

10.3390/s19204427 ◽

2019 ◽

Vol 19 (20) ◽

pp. 4427

Author(s):

Xu ◽

Wu ◽

Yu ◽

Guang

Keyword(s):

Array Signal Processing ◽

Linear Array ◽

Estimation Method ◽

Direction Of Arrival ◽

Doa Estimation ◽

Circular Array ◽

Source Signal ◽

Random Sample Consensus ◽

Esprit Algorithm ◽

Source Signals

Estimating the Direction of Arrival (DOA) is a basic and crucial problem in array signal processing. The existing DOA methods fail to obtain reliable and accurate results when noise and reverberation occur in real applications. In this paper, an accurate and robust estimation method for estimating the DOA of sources signal is proposed. Incorporating the Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm with the RANdom SAmple Consensus (RANSAC) algorithm gives rise to the RAN-ESPRIT method, which removes outliers automatically in noise-corrupted environments. In this work, a uniform circular array (UCA) is converted into a virtual uniform linear array (ULA) to begin with. Then, the covariance matrix of the received signals of the virtual linear array is reconstructed, and the ESPRIT algorithm is deployed to estimate initial DOA of the source signal. Finally, the modified RANSAC method with automatically selected thresholds is used to fit the source signal to obtain accurate DOA. The proposed method can remove the unreliable DOA feature data and leads to more accuracy of DOA estimation of source signals in reverberation environments. Experimental results demonstrate that the proposed method is more robust and efficient compared to the traditional methods (i.e., ESPRIT, TLS-ESPRIT).

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A Low-Complexity ESPRIT-Based DOA Estimation Method for Co-Prime Linear Arrays

Sensors ◽

10.3390/s16091367 ◽

2016 ◽

Vol 16 (9) ◽

pp. 1367 ◽

Cited By ~ 33

Author(s):

Fenggang Sun ◽

Bin Gao ◽

Lizhen Chen ◽

Peng Lan

Keyword(s):

Estimation Method ◽

Doa Estimation ◽

Low Complexity ◽

Linear Arrays

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Gridless DOA Estimation for Minimum-Redundancy Linear Array in Nonuniform Noise

Mathematical Problems in Engineering ◽

10.1155/2020/1580391 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Changyun Qi ◽

Gong Zhang ◽

Jiawen Yuan

Keyword(s):

Covariance Matrix ◽

Linear Array ◽

Estimation Method ◽

Doa Estimation ◽

Residual Energy ◽

Estimation Accuracy ◽

Two Stages ◽

Signal Covariance Matrix ◽

Toeplitz Structure ◽

True Signal

A gridless direction-of-arrival (DOA) estimation method to improve the estimation accuracy and resolution in nonuniform noise is proposed in this paper. This algorithm adopts the structure of minimum-redundancy linear array (MRA) and can be composed of two stages. In the first stage, by minimizing the rank of the covariance matrix of the true signal, the covariance matrix that filters out nonuniform noise is obtained, and then a gridless residual energy constraint scheme is designed to reconstruct the signal covariance matrix of the Hermitian Toeplitz structure. Finally, the unknown DOAs can be determined from the recovered covariance matrix, and the number of sources can be acquired as a byproduct. The proposed algorithm can be regarded as a gridless version method based on sparsity. Simulation results indicate that the proposed method has higher estimation accuracy and resolution compared with existing algorithms.

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

Sensors ◽

10.3390/s20010218 ◽

2019 ◽

Vol 20 (1) ◽

pp. 218 ◽

Cited By ~ 1

Author(s):

Wei He ◽

Xiao Yang ◽

Yide Wang

Keyword(s):

Computational Complexity ◽

Degrees Of Freedom ◽

Estimation Method ◽

Doa Estimation ◽

Low Complexity ◽

Covariance Matrices ◽

Linear Arrays ◽

Estimation Performance ◽

Uniform Property ◽

Finding Method

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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