3D Direction of Arrival Estimation: An Innovative Deep Neural Network Approach-Reference-Cited by-同舟云学术

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3D Direction of Arrival Estimation: An Innovative Deep Neural Network Approach

Published:2024-06-26 Issue: Volume: Page:01-04
ISSN:
Container-title:2024 13th International Conference on Modern Circuits and Systems Technologies (MOCAST)
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Author:

Mylonakis Constantinos M.¹,Velanas Pantelis²,Lazaridis Pavlos I.³,Sarigiannidis Panagiotis⁴,Goudos Sotirios K.⁵,Zaharis Zaharias D.¹

Affiliation:

1. Electrical & Computer Engineering Aristotle, University of Thessaloniki,Thessaloniki,Greece

2. Acceligence Ltd,Nicosia,Cyprus

3. School of Computing and Engineering, University of Huddersfield,Huddersfield,U.K

4. Informatics & Telecommunications Engineering, University of Western Macedonia,Kozani,Greece

5. ELEDIA@AUTH, School of Physics Aristotle University of Thessaloniki,Thessaloniki,Greece

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10615233/10615286/10615339.pdf?arnumber=10615339

Reference8 articles.

1. High-resolution frequency-wavenumber spectrum analysis

2. Multiple emitter location and signal parameter estimation

3. Robust Source Counting and DOA Estimation Using Spatial Pseudo-Spectrum and Convolutional Neural Network

4. Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System

5. Deep Convolution Network for Direction of Arrival Estimation With Sparse Prior

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