TY - GEN
T1 - A Semi-Supervised Unmanned Aerial Vehicle Recognition Method Based on Self-Distillation
AU - Zhao, Haoyu
AU - Zhang, Yan
AU - Ke, Yang
AU - Zhang, Wancheng
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2025/5/29
Y1 - 2025/5/29
N2 - The widespread use of the unmanned aerial vehicle (UAV) poses a threat to public safety. Therefore the recognition of UAV becomes more and more important. Existing deep learning based UAV signal recognition methods rely on a large number of labeled samples and perform poorly when the labeled training samples are small. To solve these problems, this paper proposes a framework for UAV recognition based on self-distillation. And a vision transformer (ViT) based time-frequency encoder is proposed to extract the features of UAV signals. The proposed method can make good use of unlabeled samples and thus maintains better performance when there are fewer labeled samples. Simulation results show that the recognition accuracy of our proposed method with fewer labeled samples is better than the recently reported works.
AB - The widespread use of the unmanned aerial vehicle (UAV) poses a threat to public safety. Therefore the recognition of UAV becomes more and more important. Existing deep learning based UAV signal recognition methods rely on a large number of labeled samples and perform poorly when the labeled training samples are small. To solve these problems, this paper proposes a framework for UAV recognition based on self-distillation. And a vision transformer (ViT) based time-frequency encoder is proposed to extract the features of UAV signals. The proposed method can make good use of unlabeled samples and thus maintains better performance when there are fewer labeled samples. Simulation results show that the recognition accuracy of our proposed method with fewer labeled samples is better than the recently reported works.
KW - attention mechanism
KW - self-supervised learning (SSL)
KW - Unmanned aerial vehicle recognition
KW - vision transformer (ViT)
UR - http://www.scopus.com/pages/publications/105010690249
U2 - 10.1145/3708657.3708718
DO - 10.1145/3708657.3708718
M3 - Conference contribution
AN - SCOPUS:105010690249
T3 - ICCIP 2024 - 2024 the 10th International Conference on Communication and Information Processing
SP - 380
EP - 385
BT - ICCIP 2024 - 2024 the 10th International Conference on Communication and Information Processing
PB - Association for Computing Machinery, Inc
T2 - 10th International Conference on Communication and Information Processing, ICCIP 2024
Y2 - 14 November 2024 through 17 November 2024
ER -