Direction-of-Arrival Estimation Based on Deep Neural Networks With Robustness to Array Imperfections

Lacking of adaptation to various array imperfections is an open problem for most high-precision direction-of-arrival (DOA) estimation methods. Machine learning-based methods are data-driven, they do not rely on prior assumptions about array geometries, and are expected to adapt better to array imper...

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Bibliographic Details
Published in:	IEEE transactions on antennas and propagation Vol. 66; no. 12; pp. 7315 - 7327
Main Authors:	Liu, Zhang-Meng, Zhang, Chenwei, Yu, Philip S.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.12.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptation Adaptive arrays Array imperfection Array signal processing Arrays Artificial neural networks Classification Classifiers Computer simulation deep neural network (DNN) Defects Direction of arrival direction-of-arrival (DOA) estimation Direction-of-arrival estimation Estimation Machine learning Multilayers multitask autoencoder Neural networks one-versus-all classification Spatial filtering supervised learning
ISSN:	0018-926X, 1558-2221
Online Access:	Get full text
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