scholarly journals 2-D Unitary ESPRIT-Like Direction-of-Arrival (DOA) Estimation for Coherent Signals with a Uniform Rectangular Array

Sensors ◽  
2013 ◽  
Vol 13 (4) ◽  
pp. 4272-4288 ◽  
Author(s):  
Shiwei Ren ◽  
Xiaochuan Ma ◽  
Shefeng Yan ◽  
Chengpeng Hao
Keyword(s):  
Direction Of Arrival ◽  
Doa Estimation ◽  
Coherent Signals ◽  
Rectangular Array ◽  
Uniform Rectangular Array

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

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4320 ◽  
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.

scholarly journals AR Model-Based Direction-of-Arrival Estimation of Coherent Signals in the Presence of Unknown Mutual Coupling

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Zhi-Chao Sha ◽  
Zhang-Meng Liu ◽  
Zhi-Tao Huang ◽  
Yi-Yu Zhou
Keyword(s):  
Mutual Coupling ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Ar Model ◽  
Estimation Accuracy ◽  
Direction Of Arrival Estimation ◽  
Coherent Signals ◽  
Model Based ◽  
Mixing Matrix ◽  
Simulation Results

This paper addresses the problem of direction-of-arrival (DOA) estimation of coherent signals in the presence of unknown mutual coupling, and an autoregression (AR) model-based method is proposed. The effects of mutual coupling can be eliminated by the inherent mechanism of the proposed algorithm, so the DOAs can be accurately estimated without any calibration sources. After the mixing matrix is estimated by independent component analysis (ICA), several parameter equations are established upon the mixing matrix. Finally, all DOAs of coherent signals are estimated by solving these equations. Compared with traditional methods, the proposed method has higher angle resolution and estimation accuracy. Simulation results demonstrate the effectiveness of the algorithm.

scholarly journals Compressed Sensing PARALIND Decomposition-Based Coherent Angle Estimation for Uniform Rectangular Array

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Wu Wei ◽  
Xu Le ◽  
Zhang Xiaofei ◽  
Li Jianfeng
Keyword(s):  
Compressed Sensing ◽  
Data Storage ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Spatial Smoothing ◽  
Rectangular Array ◽  
Propagator Method ◽  
Coherent Sources ◽  
2D Doa Estimation ◽  
Uniform Rectangular Array

In this paper, the topic of coherent two-dimensional direction of arrival (2D-DOA) estimation is investigated. Our study jointly utilizes the compressed sensing (CS) technique and the parallel profiles with linear dependencies (PARALIND) model and presents a 2D-DOA estimation algorithm for coherent sources with the uniform rectangular array. Compared to the traditional PARALIND decomposition, the proposed algorithm owns lower computational complexity and smaller data storage capacity due to the process of compression. Besides, the proposed algorithm can obtain autopaired azimuth angles and elevation angles and can achieve the same estimation performance as the traditional PARALIND, which outperforms some familiar algorithms presented for coherent sources such as the forward backward spatial smoothing-estimating signal parameters via rotational invariance techniques (FBSS-ESPRIT) and forward backward spatial smoothing-propagator method (FBSS-PM). Extensive simulations are provided to validate the effectiveness of the proposed CS-PARALIND algorithm.

scholarly journals Utilizing Principal Singular Vectors for 2D DOA Estimation in Single Snapshot Case with Uniform Rectangular Array

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yuntao Wu ◽  
Xiaobing Pei ◽  
Hing Cheung So
Keyword(s):  
Iterative Procedure ◽  
Linear Prediction ◽  
Weighted Least Squares ◽  
Doa Estimation ◽  
Data Matrix ◽  
Rectangular Array ◽  
Singular Vectors ◽  
2D Doa Estimation ◽  
Single Snapshot ◽  
Uniform Rectangular Array

The problem of azimuth and elevation directions of arrival (DOAs) estimation using a uniform rectangular array (URA) in single snapshot case is addressed in this paper. Using the principal singular vectors of the observed data matrix, an iterative procedure based on the linear prediction property, and weighted least squares is proposed for finding the DOAs with lower computational complexity. Furthermore, the azimuth and elevation parameters are automatically paired. Computer simulations are included to demonstrate the effectiveness of the proposed algorithm.

scholarly journals Two-Dimensional Direction of Arrival (DOA) Estimation for Rectangular Array via Compressive Sensing Trilinear Model

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Huaxin Yu ◽  
Xiaofeng Qiu ◽  
Xiaofei Zhang ◽  
Chenghua Wang ◽  
Gang Yang
Keyword(s):  
Compressive Sensing ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Rectangular Array ◽  
Angle Estimation ◽  
Estimation Performance ◽  
Trilinear Model ◽  
2D Doa Estimation

We investigate the topic of two-dimensional direction of arrival (2D-DOA) estimation for rectangular array. This paper links angle estimation problem to compressive sensing trilinear model and derives a compressive sensing trilinear model-based angle estimation algorithm which can obtain the paired 2D-DOA estimation. The proposed algorithm not only requires no spectral peak searching but also has better angle estimation performance than estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Furthermore, the proposed algorithm has close angle estimation performance to trilinear decomposition. The proposed algorithm can be regarded as a combination of trilinear model and compressive sensing theory, and it brings much lower computational complexity and much smaller demand for storage capacity. Numerical simulations present the effectiveness of our approach.

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