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A Tailored cGAN SAR Synthetic Data Augmentation Method for ATR Application
Aeronautics Institute of Technology, Brazil.
Aeronautics Institute of Technology, Brazil.
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0002-6643-312X
2023 (English)In: Proceedings of the IEEE Radar Conference, Institute of Electrical and Electronics Engineers (IEEE), 2023, Vol. 2023Conference paper, Published paper (Refereed)
Abstract [en]

This article proposes a method to simulate Synthetic Aperture Radar (SAR) targets for specific incidence and azimuth angles. Images synthesized by Electromagnetic Computing (EMC) are used to train a Conditional Generative Adversarial Network (cGAN). Two synthetic image chips of the same class and incidence angle, separated by two degrees in azimuth, are used as input to the cGAN. The cGAN predicts the image of the same class and incidence angle whose azimuth angle corresponds to the bisector of the two input chips. An evaluation using the SAMPLE dataset was performed to verify the quality of the image prediction. Running through a total of 100 training epochs, the cGAN converges, reaching the best Mean Squared Error (MSE) after 77 epochs. The results demonstrate that the proposed method is promising for Automatic Target Recognition (ATR) applications. © 2023 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023. Vol. 2023
Series
IEEE International Conference on Radar (RADAR), ISSN 1097-5764, E-ISSN 2640-7736
Keywords [en]
Automatic Target Recognition, Conditional Generative Adversarial Network, Data Augmentation, Image Translation, Synthetic Aperture Radar, Generative adversarial networks, Mean square error, Radar imaging, Radar target recognition, Augmentation methods, Azimuth angles, Electromagnetics, Incidence angles, Radar target, Synthesised, Synthetic data
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-25216DOI: 10.1109/RadarConf2351548.2023.10149587ISI: 001031599600049Scopus ID: 2-s2.0-85163791888ISBN: 9781665436694 (print)OAI: oai:DiVA.org:bth-25216DiVA, id: diva2:1786008
Conference
2023 IEEE Radar Conference, RadarConf23, San Antonia, 1 May 2023 5 May 2023
Available from: 2023-08-07 Created: 2023-08-07 Last updated: 2023-08-24Bibliographically approved

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Pettersson, Mats

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