A modified likelihood function approach to DOA estimation in the presence of unknown spatially correlated Gaussian noise using a uniform linear array

This paper considers the problem of estimating the direction of arrival (DOA) for the both incoherent and coherent signals from narrowband sources, located in the far field in the case of uniform linear array sensors. Three different methods are analyzed. Specifically, these methods are Music, Root-Music and ESPRIT. The pros and cons of these methods are identified and compared in light of different viewpoints. The performance of the three methods is evaluated, analytically, when possible, and by Matlab simulation. This paper can be a roadmap for beginners in understanding the basic concepts of DOA estimation issues, properties and performance.

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A covariance matrix shrinkage method with Toeplitz rectified target for DOA estimation under the uniform linear array

AEU - International Journal of Electronics and Communications ◽

10.1016/j.aeue.2017.06.026 ◽

2017 ◽

Vol 81 ◽

pp. 50-55 ◽

Cited By ~ 8

Author(s):

Yanyan Liu ◽

Xiaoying Sun ◽

Shishun Zhao

Keyword(s):

Covariance Matrix ◽

Linear Array ◽

Doa Estimation ◽

Uniform Linear Array ◽

Matrix Shrinkage ◽

Shrinkage Method

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Robust DOA estimation and array calibration in the presence of mutual coupling for uniform linear array

Science in China Series F Information Sciences ◽

10.1360/02yf0520 ◽

2004 ◽

Vol 47 (3) ◽

pp. 348 ◽

Cited By ~ 14

Author(s):

Buhong WANG

Keyword(s):

Mutual Coupling ◽

Linear Array ◽

Doa Estimation ◽

Uniform Linear Array ◽

Array Calibration

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Compressed Sensing-Based DOA Estimation with Unknown Mutual Coupling Effect

Electronics ◽

10.3390/electronics7120424 ◽

2018 ◽

Vol 7 (12) ◽

pp. 424 ◽

Cited By ~ 11

Author(s):

Peng Chen ◽

Zhenxin Cao ◽

Zhimin Chen ◽

Linxi Liu ◽

Man Feng

Keyword(s):

Compressed Sensing ◽

Mutual Coupling ◽

Linear Array ◽

State Of The Art ◽

Coupling Effect ◽

Estimation Method ◽

Doa Estimation ◽

System Model ◽

Reconstruction Problem ◽

Uniform 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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DOA Estimation of Non-circular Source for Large Uniform Linear Array with a Single Snapshot: Extended DFT Method

IEEE Communications Letters ◽

10.1109/lcomm.2021.3120211 ◽

2021 ◽

pp. 1-1

Author(s):

Baobao Li ◽

Xiaofei Zhang ◽

Jianfeng Li ◽

Penghui Ma

Keyword(s):

Linear Array ◽

Dft Method ◽

Doa Estimation ◽

Uniform Linear Array ◽

Circular Source ◽

Single Snapshot

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DOA Estimation for Antenna Array with Partially-Well Sensors via Low-Rank Matrix Completion

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3977 ◽

2013 ◽

Vol 756-759 ◽

pp. 3977-3981 ◽

Cited By ~ 3

Author(s):

Hua Xing Yu ◽

Xiao Fei Zhang ◽

Jian Feng Li ◽

De Ben

Keyword(s):

Missing Values ◽

Linear Array ◽

Matrix Completion ◽

Doa Estimation ◽

Low Rank ◽

Uniform Linear Array ◽

Completion Problem ◽

Rank Matrix ◽

Low Rank Matrix Completion ◽

Low Rank Matrix

In this paper, we address the angle estimation problem in linear array with some ill sensors (partially-well sensors), which only work well randomly. The output of the array will miss some values, and this can be regarded as a low-rank matrix completion problem due to the property that the number of sources is smaller than the number of the total sensors. The output of the array, which is corrupted by the missing values and the noise, can be complete via the Optspace method, and then the angles can be estimated according to the complete output. The proposed algorithm works well for the array with some ill sensors; moreover, it is suitable for non-uniform linear array. Simulation results illustrate performance of the algorithm.

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