Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/9016
Title: A Fast Polarization Estimation Method with Convolutional Neural Networks
Authors: Onur Y.
Hayvaci H.T.
Keywords: Convolutional neural networks
Cost benefit analysis
Electronic warfare
Military applications
Multiple signal classification
Computational costs
Computational performance
Convolutional neural network
Cost analysis
Estimation methods
Performances analysis
Polarization estimations
Radar signals
Return signals
Subspace-based algorithms
Polarization
Issue Date: 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: In this study, we propose a new method with an artificial intelligence infrastructure to estimate polarization of a radar signal. In modern electronic warfare and radar systems polarization of the return signal now poses essential information. Subspace-based algorithms such as MUSIC and ESPRIT have high computational costs for estimating polarization. Computational cost and performance analysis of the proposed method is conducted via simulations and results are discussed along with the existing solutions such as MUSIC algorithm. Simulation results show that the proposed algorithm reduce the computational cost compared to classical MUSIC. The proposed algorithm also reduce the polarization estimation error in low SNR scenarios. © 2022 IEEE.
Description: 9th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2022 -- 27 June 2022 through 29 June 2022 -- -- 182210
URI: https://doi.org/10.1109/MetroAeroSpace54187.2022.9855981
https://hdl.handle.net/20.500.11851/9016
ISBN: 9.78167E+12
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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