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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Vydáno v:IEEE transactions on antennas and propagation Ročník 66; číslo 12; s. 7315 - 7327
Hlavní autoři: Liu, Zhang-Meng, Zhang, Chenwei, Yu, Philip S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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 imperfections when compared with model-based counterparts. This paper introduces a framework of the deep neural network to address the DOA estimation problem, so as to obtain good adaptation to array imperfections and enhanced generalization to unseen scenarios. The framework consists of a multitask autoencoder and a series of parallel multilayer classifiers. The autoencoder acts like a group of spatial filters, it decomposes the input into multiple components in different spatial subregions. These components thus have more concentrated distributions than the original input, which helps to reduce the burden of generalization for subsequent DOA estimation classifiers. The classifiers follow a one-versus-all classification guideline to determine if there are signal components near preseted directional grids, and the classification results are concatenated to reconstruct a spatial spectrum and estimate signal directions. Simulations are carried out to show that the proposed method performs satisfyingly in both generalization and imperfection adaptation.
Bibliografie:ObjectType-Article-1
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ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2018.2874430