{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:48:42Z","timestamp":1767340122652,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T00:00:00Z","timestamp":1697673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A20161"],"award-info":[{"award-number":["U20A20161"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To improve communication stability, more wireless devices transmit multi-modal signals while operating. The term \u2018modal\u2019 refers to signal waveforms or signal types. This poses challenges to traditional specific emitter identification (SEI) systems, e.g., unknown modal signals require extra open-set mode identification; different modes require different radio frequency fingerprint (RFF) extractors and SEI classifiers; and it is hard to collect and label all signals. To address these issues, we propose an enhanced SEI system consisting of a universal RFF extractor, denoted as multiple synchrosqueezed wavelet transformation of energy unified (MSWTEu), and a new generative adversarial network for feature transferring (FTGAN). MSWTEu extracts uniform RFF features for different modal signals, FTGAN transfers different modal features to a recognized distribution in an unsupervised manner, and a novel training strategy is proposed to achieve emitter identification across multi-modal signals using a single clustering method. To evaluate the system, we built a hybrid dataset, which consists of multi-modal signals transmitted by various emitters, and built a complete civil air traffic control radar beacon system (ATCRBS) dataset for airplanes. The experiments show that our enhanced SEI system can resolve the SEI problems associated with crossing signal modes. It directly achieves 86% accuracy in cross-modal emitter identification using an unsupervised classifier, and simultaneously obtains 99% accuracy in open-set recognition of signal mode.<\/jats:p>","DOI":"10.3390\/s23208576","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T07:15:36Z","timestamp":1697699736000},"page":"8576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Specific Emitter Identification System Design for Crossing Signal Modes in the Air Traffic Control Radar Beacon System and Wireless Devices"],"prefix":"10.3390","volume":"23","author":[{"given":"Miyi","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Computer Science, Sichuan University, Chengdu 610065, China"},{"name":"Sichuan Jiuzhou Electric Group Co., Ltd., Mianyang 621000, China"}]},{"given":"Yue","family":"Yao","sequence":"additional","affiliation":[{"name":"Sichuan Jiuzhou Electric Group Co., Ltd., Mianyang 621000, China"}]},{"given":"Hong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Sichuan University, Chengdu 610065, China"}]},{"given":"Youzhang","family":"Hu","sequence":"additional","affiliation":[{"name":"Sichuan Jiuzhou Electric Group Co., Ltd., Mianyang 621000, China"}]},{"given":"Hongyu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Sichuan University, Chengdu 610065, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3449428","DOI":"10.1155\/2022\/3449428","article-title":"Review on Security Issues and Applications of Trust Mechanism in Wireless Sensor Networks","volume":"2022","author":"Xia","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_2","first-page":"2519","article-title":"Detection of transient in radio frequency fingerprinting using signal phase","volume":"9","author":"Hall","year":"2003","journal-title":"Wirel. Opt. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kennedy, I.O., Scanlon, P., Mullany, F.J., Buddhikot, M.M., Nolan, K.E., and Rondeau, T.W. (2008, January 21\u201324). Radio Transmitter Fingerprinting: A Steady State Frequency Domain Approach. Proceedings of the 2008 IEEE 68th Vehicular Technology Conference, Calgary, AB, Canada.","DOI":"10.1109\/VETECF.2008.291"},{"key":"ref_4","unstructured":"Defense Advanced Research Projects Agency (DARPA) (2017, August 11). Radio Frequency Machine Learning Systems (RFMLS) (HR001117S0043) [EB\/OL], Available online: http:\/\/www.fbo.gov\/."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"21248","DOI":"10.1109\/JSEN.2022.3208518","article-title":"Classification of UAVs Utilizing Fixed Boundary Empirical Wavelet Sub-Bands of RF Fingerprints and Deep Convolutional Neural Network","volume":"22","author":"Bremnes","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13540","DOI":"10.1109\/JSEN.2021.3068444","article-title":"UAV Detection and Identification Based on WiFi Signal and RF Fingerprint","volume":"21","author":"Nie","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8356","DOI":"10.1109\/JIOT.2020.3045305","article-title":"Radio Identity Verification-Based IoT Security Using RF-DNA Fingerprints and SVM","volume":"8","author":"Reising","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huang, D.Y., Hou, C.P., Yang, Y., Lang, Y., and Wang, Q. (2018, January 23\u201327). Micro-Doppler Spectrogram Denoising Based on Generative Adversarial Network. Proceedings of the 48th European Microwave Conference (EuMC), Madrid, Spain.","DOI":"10.23919\/EuMC.2018.8541507"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.1007\/s11277-020-07162-z","article-title":"Performance Analysis of Modular RF Front End for RF Fingerprinting of Bluetooth Devices","volume":"112","author":"Uzundurukan","year":"2020","journal-title":"Wirel. Pers. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11276","DOI":"10.1109\/JIOT.2021.3051402","article-title":"A Robust Radio-Frequency Fingerprint Extraction Scheme for Practical Device Recognition","volume":"8","author":"Zhou","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Agadakos, I., Agadakos, N., Polakis, J., and Amer, M.R. (2020, January 16\u201318). Chameleons\u2019 Oblivion: Complex-Valued Deep Neural Networks for Protocol-Agnostic RF Device Fingerprinting. Proceedings of the 5th IEEE European Symposium on Security and Privacy (IEEE Euro S and P), Electr Network, Genova, Italy.","DOI":"10.1109\/EuroSP48549.2020.00028"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1109\/JSAC.2021.3087243","article-title":"An Efficient Specific Emitter Identification Method Based on ComplexValued Neural Networks and Network Compression","volume":"39","author":"Wang","year":"2021","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"25117","DOI":"10.1109\/JIOT.2022.3195450","article-title":"An Adaptive Specific Emitter Identification System for Dynamic Noise Domain","volume":"9","author":"Zeng","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Yi, Z., Li, S., and Li, L. (2022). Deep Muti-Modal Generic Representation Auxiliary Learning Networks for End-to-End Radar Emitter Classification. Aerospace, 9.","DOI":"10.3390\/aerospace9110732"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Leonardi, M., and Gerardi, F. (2020). Aircraft Mode S Transponder Fingerprinting for Intrusion Detection. Aerospace, 7.","DOI":"10.3390\/aerospace7030030"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nicolussi, A., Tanner, S., and Wattenhofer, R. (2021, January 23\u201327). Aircraft Fingerprinting Using Deep Learning. Proceedings of the 28th European Signal Processing Conference (EUSIPCO), Electr Network, Dublin, Ireland.","DOI":"10.23919\/Eusipco47968.2020.9287691"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Leonardi, M., Di Gregorio, L., and Di Fausto, D. (2017). Air Traffic Security: Aircraft Classification Using ADS-B Message\u2019s Phase-Pattern. Aerospace, 4.","DOI":"10.3390\/aerospace4040051"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Al-Shawabka, A., Restuccia, F., D\u2019Oro, S., Jian, T., Rendon, B.C., Soltani, N., Dy, J., Ioannidis, S., Chowdhury, K., and Melodia, T. (2020, January 6\u20139). Exposing the Fingerprint: Dissecting the Impact of the Wireless Channel on Radio Fingerprinting. Proceedings of the IEEE INFOCOM 2020\u2014IEEE Conference on Computer Communications, Toronto, ON, Canada.","DOI":"10.1109\/INFOCOM41043.2020.9155259"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1109\/TMTT.2010.2098041","article-title":"Physically Inspired Neural Network Model for RF Power Amplifier Behavioral Modeling and Digital Predistortion","volume":"59","author":"Mkadem","year":"2011","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1109\/ACCESS.2019.2962626","article-title":"Specific Emitter Identification Techniques for the Internet of Things","volume":"8","author":"Sa","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kuzdeba, S., Robinson, J., and Carmack, J. (2021, January 9\u201312). Transfer Learning with Radio Frequency Signals. Proceedings of the 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA.","DOI":"10.1109\/CCNC49032.2021.9369550"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tang, H., and Jia, K. (2020, January 3). Discriminative adversarial domain adaptation. Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i04.6054"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.acha.2010.08.002","article-title":"Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool","volume":"30","author":"Daubechies","year":"2011","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6786","DOI":"10.1109\/JIOT.2019.2911347","article-title":"A Robust RF Fingerprinting Approach Using Multisampling Convolutional Neural Network","volume":"6","author":"Yu","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"91913","DOI":"10.1109\/ACCESS.2021.3092435","article-title":"A Deep Learning-Based Human Identification System with Wi-Fi CSI Data Augmentation","volume":"9","author":"Mo","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1109\/TSMCB.2012.2227469","article-title":"Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach","volume":"43","author":"Xue","year":"2013","journal-title":"IEEE Trans. Cybern."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1109\/LSP.2020.2978333","article-title":"Radio Frequency Fingerprint Extraction Based on Multi-Dimension Approximate Entropy","volume":"27","author":"Sun","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"18008","DOI":"10.1109\/JSEN.2022.3195065","article-title":"Few-Shot Unsupervised Specific Emitter Identification Based on Density Peak Clustering Algorithm and Meta-Learning","volume":"22","author":"Xie","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2898","DOI":"10.1109\/TIFS.2020.2978620","article-title":"Unsupervised Specific Emitter Identification Method Using Radio-Frequency Fingerprint Embedded InfoGAN","volume":"15","author":"Gong","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_30","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the 28th Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, H., Li, A., Han, X., Chen, Z., Zhang, Y., and Guo, Y. (2019, January 8\u201312). Improving open set domain adaptation using image-to-image translation. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Shanghai, China.","DOI":"10.1109\/ICME.2019.00219"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/20\/8576\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:09:36Z","timestamp":1760130576000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/20\/8576"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,19]]},"references-count":31,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["s23208576"],"URL":"https:\/\/doi.org\/10.3390\/s23208576","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,10,19]]}}}