scholarly journals Block Newtonised orthogonal matching pursuit for off‐grid DOA estimation in the presence of unknown mutual coupling

2021 ◽  
Author(s):  
Wen‐Gen Tang ◽  
Hong Jiang ◽  
Qi Zhang
Keyword(s):  
Mutual Coupling ◽  
Matching Pursuit ◽  
Doa Estimation ◽  
Orthogonal Matching Pursuit

Two-dimensional multi-snapshot Newtonized orthogonal matching pursuit for DOA estimation

2021 ◽  
pp. 103313
Author(s):  
Lin Han ◽  
Xingchuan Liu ◽  
Ning Zhang ◽  
Sheng Wu ◽  
Jiang Zhu ◽  
...  
Keyword(s):  
Matching Pursuit ◽  
Doa Estimation ◽  
Orthogonal Matching Pursuit ◽  
Two Dimensional

scholarly journals DoA Estimation Using Neural Tangent Kernel under Electromagnetic Mutual Coupling

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1057
Author(s):  
Qifeng Wang ◽  
Xiaolin Hu ◽  
Xiaobao Deng ◽  
Nicholas E. Buris
Keyword(s):  
Mutual Coupling ◽  
Deep Neural Network ◽  
Doa Estimation ◽  
Antenna Element ◽  
Root Finding ◽  
Multiple Signal ◽  
Estimation Problems ◽  
Limiting Case ◽  
Signal Doa Estimation ◽  
Polynomial Root Finding

Antenna element mutual coupling degrades the performance of Direction of Arrival (DoA) estimation significantly. In this paper, a novel machine learning-based method via Neural Tangent Kernel (NTK) is employed to address the DoA estimation problem under the effect of electromagnetic mutual coupling. NTK originates from Deep Neural Network (DNN) considerations, based on the limiting case of an infinite number of neurons in each layer, which ultimately leads to very efficient estimators. With the help of the Polynomial Root Finding (PRF) technique, an advanced method, NTK-PRF, is proposed. The method adapts well to multiple-signal scenarios when sources are far apart. Numerical simulations are carried out to demonstrate that this NTK-PRF approach can handle, accurately and very efficiently, multiple-signal DoA estimation problems with realistic mutual coupling.

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