A Novel Three-Dimensional Direction-of-Arrival Estimation Approach Using a Deep Convolutional Neural Network

A Novel Three-Dimensional Direction-of-Arrival Estimation Approach Using a Deep Convolutional Neural Network

This article aims to constitute a noteworthy contribution to the domain of direction-of-arrival (DoA) estimation through the application of deep learning algorithms. We approach the DoA estimation challenge as a binary classification task, employing a novel grid in the output layer and a deep convol...

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Bibliographic Details
Main Authors: Constantinos M. Mylonakis, Zaharias D. Zaharis
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of Vehicular Technology
Subjects:
Direction-of-arrival (DoA) estimation
convolutional neural network (CNN)
deep learning (DL)
binary classification
antenna array analysis and synthesis
spatial signal processing
Online Access:https://ieeexplore.ieee.org/document/10504989/
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Summary:This article aims to constitute a noteworthy contribution to the domain of direction-of-arrival (DoA) estimation through the application of deep learning algorithms. We approach the DoA estimation challenge as a binary classification task, employing a novel grid in the output layer and a deep convolutional neural network (DCNN) as the classifier. The input of the DCNN is the correlation matrix of signals received by a <inline-formula><tex-math notation="LaTeX">$4 \times 4$</tex-math></inline-formula> uniformly spaced patch antenna array. The proposed model&#x0027;s performance is evaluated based on its capacity to predict angles of arrival from any direction in a three-dimensional space, encompassing azimuth angles within the interval <inline-formula><tex-math notation="LaTeX">$[0^\circ, 360^\circ)$</tex-math></inline-formula> and polar angles within <inline-formula><tex-math notation="LaTeX">$[0^\circ, 60^\circ ]$</tex-math></inline-formula>. We aim to optimize the utilization of spatial information and create a robust, precise, and efficient DoA estimator. To address this, we conduct comprehensive testing in diverse scenarios, encompassing the simultaneous reception of multiple signals across a wide range of signal-to-noise ratio values. Both mean absolute error and root mean squared error are calculated to assess the performance of the DCNN. Rigorous comparison with conventional and state-of-the-art endeavors emphasizes the proposed model&#x0027;s efficacy.
ISSN:2644-1330

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