Version 1
: Received: 29 August 2024 / Approved: 29 August 2024 / Online: 29 August 2024 (16:13:02 CEST)
How to cite:
Xiao, J.; Zhang, H.; Shao, Z.; Zheng, Y.; Ding, W. Progressive Unsupervised Domain Adaptation for RF Signal Attribute Recognition across Communication Scenarios. Preprints2024, 2024082188. https://doi.org/10.20944/preprints202408.2188.v1
Xiao, J.; Zhang, H.; Shao, Z.; Zheng, Y.; Ding, W. Progressive Unsupervised Domain Adaptation for RF Signal Attribute Recognition across Communication Scenarios. Preprints 2024, 2024082188. https://doi.org/10.20944/preprints202408.2188.v1
Xiao, J.; Zhang, H.; Shao, Z.; Zheng, Y.; Ding, W. Progressive Unsupervised Domain Adaptation for RF Signal Attribute Recognition across Communication Scenarios. Preprints2024, 2024082188. https://doi.org/10.20944/preprints202408.2188.v1
APA Style
Xiao, J., Zhang, H., Shao, Z., Zheng, Y., & Ding, W. (2024). Progressive Unsupervised Domain Adaptation for RF Signal Attribute Recognition across Communication Scenarios. Preprints. https://doi.org/10.20944/preprints202408.2188.v1
Chicago/Turabian Style
Xiao, J., Yikai Zheng and Wenrui Ding. 2024 "Progressive Unsupervised Domain Adaptation for RF Signal Attribute Recognition across Communication Scenarios" Preprints. https://doi.org/10.20944/preprints202408.2188.v1
Abstract
As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making the emitter identification in remote sensing practices more essential. Effective recognition of Radio Frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to the diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a Progressive Maximum Similarity-based Unsupervised Domain Adaptation (PMS-UDA) method for RF signal attribute recognition. Initially, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenario. Experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as cross both ground-to-ground and air-to-ground scenarios, compared to baseline methods.
Keywords
progressive maximum similarity; unsupervised domain adaptation; radio frequency signal attribute recognition; automatic modulation recognition; radio frequency fingerprint identification; signal processing
Subject
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
