Author:
Zhao Yurui,Wang Xiang,Sun Liting,Huang Zhitao
Abstract
AbstractExtensive experiments illustrate that moments and their derivations can act as effective fingerprint features for specific emitter identification. Nevertheless, the lack of mechanistic explanation restricts the moment-based fingerprint features to a trial-based and data-driven technique. To make up for theoretical weakness and enhance generalization ability, we analytically investigate how intentional modulation and unintentional modulation affect moments. A framework for extracting moment-based fingerprint features is proposed through fine-segmenting slices. Fingerprint features are extracted, followed by segmenting signals into a combination of sinewaves and calculating their moments. The proposed framework shows advantages in mechanism interpretability and generalizing ability. Simulations and experiments verified the correctness and effectiveness of the proposed framework.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Reference21 articles.
1. A. Jagannath, J. Jagannath, , P.S.P.V. Kumar, A comprehensive survey on radio frequency (rf) fingerprinting: traditional approaches, deep learning, and open challenges (2022). arXiv:2201.00680
2. J.M. McGinthy, L.J. Wong, A.J. Michaels, Groundwork for neural network-based specific emitter identification authentication for IoT. IEEE Internet Things J. 6(4), 6429–6440 (2019)
3. J. Han, T. Zhang, D. Ren, X. Zheng, Mechanism analysis and feature extraction algorithm of communication emitter fingerprint. AEU-Int. J. Electron. Commun. 106, 89–95 (2019)
4. J. Hall, M. Barbeau, E. Kranakis, et al., Detection of transient in radio frequency fingerprinting using signal phase. Wirel. Opt. Commun. 13–18 (2003)
5. Y. Jia, S. Zhu, L. Gan, Specific emitter identification based on the natural measure. Entropy 19(3), 117 (2017)
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