{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T16:22:14Z","timestamp":1783786934152,"version":"3.55.0"},"reference-count":35,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T00:00:00Z","timestamp":1728000000000},"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":["U20B2042"],"award-info":[{"award-number":["U20B2042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making 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 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. First, 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, a domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenarios. The experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as across both ground-to-ground and air-to-ground scenarios, compared to baseline methods.<\/jats:p>","DOI":"10.3390\/rs16193696","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T06:00:57Z","timestamp":1728021657000},"page":"3696","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Progressive Unsupervised Domain Adaptation for Radio Frequency Signal Attribute Recognition across Communication Scenarios"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0573-8106","authenticated-orcid":false,"given":"Jing","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8356-9328","authenticated-orcid":false,"given":"Hang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6262-0470","authenticated-orcid":false,"given":"Zeqi","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9127-2390","authenticated-orcid":false,"given":"Yikai","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5490-4724","authenticated-orcid":false,"given":"Wenrui","family":"Ding","sequence":"additional","affiliation":[{"name":"Institute of Unmanned System, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Labib, N.S., Danoy, G., Musial, J., Brust, M.R., and Bouvry, P. (2019, January 25\u201329). A Multilayer Low-Altitude Airspace Model for UAV Traffic Management. Proceedings of the 9th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet \u201919, Miami Beach, FL, USA.","DOI":"10.1145\/3345838.3355998"},{"key":"ref_2","first-page":"1399","article-title":"Maximizing the Value of America\u2019s Newest Resource, Low-Altitude Airspace: An Economic Analysis of Aerial Trespass and Drones","volume":"95","author":"Watson","year":"2020","journal-title":"Indiana Law J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, L., Tian, T., and Yin, J. (2021). A Review of Unmanned Aerial Vehicle Low-Altitude Remote Sensing (UAV-LARS) Use in Agricultural Monitoring in China. Remote Sens., 13.","DOI":"10.3390\/rs13061221"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yi, J., Zhang, H., Wang, F., Ning, C., Liu, H., and Zhong, G. (2023). An Operational Capacity Assessment Method for an Urban Low-Altitude Unmanned Aerial Vehicle Logistics Route Network. Drones, 7.","DOI":"10.3390\/drones7090582"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.cja.2020.05.011","article-title":"UAV Navigation in High Dynamic Environments: A Deep Reinforcement Learning Approach","volume":"34","author":"Guo","year":"2021","journal-title":"Chin. J. Aeronaut."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MAES.2021.3115205","article-title":"Global Analysis of Active Defense Technologies for Unmanned Aerial Vehicle","volume":"37","author":"Lyu","year":"2022","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cai, J., Gan, F., Cao, X., Liu, W., and Li, P. (2022). Radar Intra\u2013Pulse Signal Modulation Classification with Contrastive Learning. Remote Sens., 14.","DOI":"10.3390\/rs14225728"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1109\/TCCN.2023.3252580","article-title":"Towards the Automatic Modulation Classification with Adaptive Wavelet Network","volume":"9","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1109\/TNSM.2016.2572212","article-title":"An Enhanced Available Bandwidth Estimation Technique for an End-to-End Network Path","volume":"13","author":"Paul","year":"2016","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3100","DOI":"10.1109\/TSP.2019.2912132","article-title":"Adaptive Instantaneous Frequency Estimation of Multicomponent Signals Based on Linear Time\u2013Frequency Transforms","volume":"67","author":"Abdoush","year":"2019","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Huang, B., Lin, C.L., Chen, W., Juang, C.F., and Wu, X. (2020, January 22\u201324). Signal Frequency Estimation Based on RNN. Proceedings of the 2020 Chinese Control And Decision Conference (CCDC), Hefei, China.","DOI":"10.1109\/CCDC49329.2020.9164504"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sun, L., Ke, D., Wang, X., Huang, Z., and Huang, K. (2022). Robustness of Deep Learning-Based Specific Emitter Identification under Adversarial Attacks. Remote Sens., 14.","DOI":"10.3390\/rs14194996"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4243","DOI":"10.1109\/TWC.2023.3316286","article-title":"Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprinting","volume":"23","author":"Zeng","year":"2023","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/TCCN.2024.3360508","article-title":"Data-and-Channel-Independent Radio Frequency Fingerprint Extraction for LTE-V2X","volume":"10","author":"Qi","year":"2024","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1587412","DOI":"10.1155\/2017\/1587412","article-title":"Low Altitude UAV Air-to-Ground Channel Measurement and Modeling in Semiurban Environments","volume":"2017","author":"Qiu","year":"2017","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tu, K., Rodr\u00edguez-Pi\u00f1eiro, J., Yin, X., and Tian, L. (2019, January 23\u201324). Low Altitude Air-to-Ground Channel Modelling Based on Measurements in a Suburban Environment. Proceedings of the 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP), Xi\u2019an, China.","DOI":"10.1109\/WCSP.2019.8927975"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2279","DOI":"10.1007\/s10462-022-10225-1","article-title":"Internet of Low-Altitude UAVs (IoLoUA): A Methodical Modeling on Integration of Internet of \u201cThings\u201d with \u201cUAV\u201d Possibilities and Tests","volume":"56","author":"Srivastava","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/TR.2021.3062045","article-title":"Transfer Learning Promotes 6G Wireless Communications: Recent Advances and Future Challenges","volume":"70","author":"Wang","year":"2021","journal-title":"IEEE Trans. Reliab."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1109\/TWC.2020.3034895","article-title":"Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications","volume":"20","author":"Liu","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wong, L.J., and Michaels, A.J. (2022). Transfer Learning for Radio Frequency Machine Learning: A Taxonomy and Survey. Sensors, 22.","DOI":"10.3390\/s22041416"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Deng, W., Xu, Q., Li, S., Wang, X., and Huang, Z. (2023). Cross-Domain Automatic Modulation Classification Using Multimodal Information and Transfer Learning. Remote Sens., 15.","DOI":"10.3390\/rs15153886"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jing, Z., Li, P., Wu, B., Yuan, S., and Chen, Y. (2022). An Adaptive Focal Loss Function Based on Transfer Learning for Few-Shot Radar Signal Intra-Pulse Modulation Classification. Remote Sens., 14.","DOI":"10.3390\/rs14081950"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2014.2347059","article-title":"Visual Domain Adaptation: A survey of recent advances","volume":"32","author":"Patel","year":"2015","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","article-title":"Deep Visual Domain Adaptation: A Survey","volume":"312","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3400066","article-title":"A Survey of Unsupervised Deep Domain Adaptation","volume":"11","author":"Wilson","year":"2020","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Farahani, A., Voghoei, S., Rasheed, K., and Arabnia, H.R. (2021). A Brief Review of Domain Adaptation. Advances in Data Science and Information Engineering, Springer.","DOI":"10.1007\/978-3-030-71704-9_65"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/LSP.2020.2991875","article-title":"Adversarial transfer learning for deep learning based automatic modulation classification","volume":"27","author":"Bu","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, P., Guo, L., Zhao, H., Shang, P., Chu, Z., and Lu, X. (2023). A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation. Remote Sens., 15.","DOI":"10.3390\/rs15215214"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1109\/LSP.2023.3275912","article-title":"Multiscale Correlation Networks Based On Deep Learning for Automatic Modulation Classification","volume":"30","author":"Xiao","year":"2023","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_30","unstructured":"Xiao, J., Ding, W., Shao, Z., Zhang, D., Ma, Y., Wang, Y., and Wang, J. (2024). Multi-Periodicity Dependency Transformer Based on Spectrum Offset for Radio Frequency Fingerprint Identification. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/TIT.2003.813506","article-title":"A new metric for probability distributions","volume":"49","author":"Endres","year":"2003","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1214\/aoms\/1177729694","article-title":"On Information and Sufficiency","volume":"22","author":"Kullback","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.cja.2021.08.016","article-title":"Large-Scale Real-World Radio Signal Recognition with Deep Learning","volume":"35","author":"Tu","year":"2022","journal-title":"Chin. J. Aeronaut."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6607","DOI":"10.1109\/TVT.2017.2659651","article-title":"Air\u2013ground channel characterization for unmanned aircraft systems\u2014Part III: The suburban and near-urban environments","volume":"66","author":"Matolak","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1109\/JSTSP.2018.2797022","article-title":"Over-the-Air Deep Learning Based Radio Signal Classification","volume":"12","author":"Roy","year":"2018","journal-title":"IEEE J. Sel. Top. Signal Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3696\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:10:29Z","timestamp":1760112629000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3696"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,4]]},"references-count":35,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16193696"],"URL":"https:\/\/doi.org\/10.3390\/rs16193696","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,4]]}}}