Deep Learning-based Resolution Enhancement in SAR Image for Automotive Radar Sensors
- Authors
- Kang, Sung-Wook; Cho, Hahng-Jun; Lee, Hojung; Lee, Seongwook
- Issue Date
- Oct-2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- automotive radar sensor; generative adversarial network; synthetic aperture radar
- Citation
- Proceedings of IEEE Sensors
- Journal Title
- Proceedings of IEEE Sensors
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/71357
- DOI
- 10.1109/SENSORS56945.2023.10325124
- ISSN
- 1930-0395
- Abstract
- Forming high-resolution synthetic aperture radar (SAR) images requires large amounts of sampled data, which increases computation time and complexity. Therefore, in this paper, we propose a method to enhance the resolution of SAR images for automotive radar sensors using a generative adversarial network (GAN). The proposed GAN is an unsupervised image-to-image translation GAN based on a variational autoencoder and can form high-resolution SAR images from a small amount of sampled data. The SAR images formed by the proposed method are compared in terms of peak signal-to-noise ratio and structural similarity index measure for performance evaluation, and they are increased by 2.75% and 4.43%, respectively, compared to the existing low-resolution SAR images. © 2023 IEEE.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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