Deep Learning-Based Angular Resolution Improvement in Planar Sensor Array
- Authors
- Jeong, Taewon; Kang, Sung-Wook; Lee, Seongwook
- Issue Date
- Dec-2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- direction-of-arrival; DOA; generative adversarial network; GAN; planar sensor array; resolution improvement; Sensor signal processing
- Citation
- IEEE Sensors Letters, v.7, no.12, pp 1 - 4
- Pages
- 4
- Journal Title
- IEEE Sensors Letters
- Volume
- 7
- Number
- 12
- Start Page
- 1
- End Page
- 4
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/70857
- DOI
- 10.1109/LSENS.2023.3330101
- ISSN
- 2475-1472
- Abstract
- The high angular resolution, which leads to an accurate direction-of-arrival (DOA) estimation, is essential in the radar systems for target detection and localization. Therefore, we propose a generative adversarial network (GAN)-based method that improves the angular resolution in target detection images. In the proposed network, we use the U-Net and the patch discriminator as the generator and the discriminator, respectively. Then, we verify the performance of the proposed method through simulations. The mean-squared error between the image generated by the proposed deep learning network and the ground truth image is 0.004, indicating a high level of similarity. In addition, the peak signal-to-noise ratio of the image with the increased resolution is about 11 dB higher than that of the original low-resolution (LR) image. By enhancing the angular resolution through the proposed method, the accuracy of DOA estimation can be improved in radar systems. © 2017 IEEE.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/70857)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.