DoA Estimation Using Neural Tangent Kernel under Electromagnetic Mutual Coupling
| Autor: | Xiaolin Hu, Nicholas E. Buris, Xiaobao Deng, Qifeng Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
TK7800-8360
Computer Networks and Communications Computer science mutual coupling 02 engineering and technology 010501 environmental sciences 01 natural sciences direction of arrival Polynomial root finding polynomial root finding 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering 0105 earth and related environmental sciences Coupling Artificial neural network Estimator Direction of arrival Tangent 020206 networking & telecommunications Limiting case (mathematics) Hardware and Architecture Control and Systems Engineering Kernel (statistics) Signal Processing Electronics neural tangent kernel Algorithm |
| Zdroj: | Electronics, Vol 10, Iss 1057, p 1057 (2021) Electronics Volume 10 Issue 9 |
| ISSN: | 2079-9292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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