Improving DOA Estimation via an Optimal Deep Residual Neural Network Classifier on Uniform Linear Arrays

Improving DOA Estimation via an Optimal Deep Residual Neural Network Classifier on Uniform Linear Arrays

The main objective of this work is to improve and evaluate the effectiveness of the neural network (NN) architecture in the domain of estimation of direction of arrival (DOA), with an emphasis on a multi-class classification task with grid resolutions of 0.25 and 0.1. Specifically, a comprehensive a...

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Bibliographic Details
Main Authors: Haya Al Kassir, Nikolaos V. Kantartzis, Pavlos I. Lazaridis, Panagiotis Sarigiannidis, Sotirios K. Goudos, Christos G. Christodoulou, Zaharias D. Zaharis
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of Antennas and Propagation
Subjects:
synthesis
antenna optimization
machine learning
direction-of-arrival (DOA) estimation
residual neural networks
Online Access:https://ieeexplore.ieee.org/document/10421782/
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https://ieeexplore.ieee.org/document/10421782/

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