@inproceedings{3e7ce24267e440b09fa3c827984e9b2e,
title = "A Method to Obtain Deep Neural Network for Predicting ISAR Images of Coted Targets with Defect",
abstract = "The coating of radar absorbing material can reduce radar cross section of aircrafts significantly, it's indeed necessary to analyze their electromagnetic scattering characteristics. The traditional method requires plenty of time thus can't meet the need of real-time analysis. To solve this problem, this paper proposed an image-to-image deep neural network based on U-net with residual unit. This network can predict the ISAR image for a coated target with random defect. The well-trained network can accelerate the speed by five orders while ensuring a relative error lower than 0.28\%. The numerical results are exhibited to prove that the proposed method is of great efficiency and accuracy compared to the traditional method.",
keywords = "Radar absorbing material, U-net, inverse synthetic aperture radar (ISAR), prediction",
author = "Heng Cao and Jianing Cao and Qiang Ren",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2021 ; Conference date: 28-11-2021 Through 30-11-2021",
year = "2021",
doi = "10.1109/iWEM53379.2021.9790521",
language = "英语",
series = "IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE International Workshop on Electromagnetics",
address = "美国",
}