@inproceedings{2afd4e456d1347379db61138910b88ed,
title = "Identification of Active Jamming Based on Swin Transformer Model and Splitting Features",
abstract = "With the continuous development of Digital Radio Frequency Memory (DRFM) technology, radar working condition is seriously threatened by various activate jamming, echo of true target will be mixed or covered by jamming. In this condition, splitting features extracted by modulating splitting code into the process of pulse compression present greatly difference between true target and jamming, and then this paper proposes a jamming identification method based on splitting feature and Swin Transformer (shifted window Transformer) neural network which can effectively distinguish the typical jamming, achieve classification task, and improve detection performance and recognition accuracy. Finally, the verification result of measured data shows that true target and jamming can be recognized perfectly.",
keywords = "jamming identification, neural network, splitting feature, transformer",
author = "Hu, {Zi Jun} and Xinliang Chen and Zhennan Liang and Bowen Cai",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 22nd IEEE International Conference on Communication Technology, ICCT 2022 ; Conference date: 11-11-2022 Through 14-11-2022",
year = "2022",
doi = "10.1109/ICCT56141.2022.10072757",
language = "English",
series = "International Conference on Communication Technology Proceedings, ICCT",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1107--1113",
booktitle = "2022 IEEE 22nd International Conference on Communication Technology, ICCT 2022",
address = "United States",
}