{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:08:30Z","timestamp":1760148510232,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T00:00:00Z","timestamp":1683849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Technical Support Talent Plan of Chinese Academy of Science","award":["E317YR17","GK AD22035957","12273045","E016YR1R","E039SB1K"],"award-info":[{"award-number":["E317YR17","GK AD22035957","12273045","E016YR1R","E039SB1K"]}]},{"name":"Project for Guangxi Science and Technology Base and Talents","award":["E317YR17","GK AD22035957","12273045","E016YR1R","E039SB1K"],"award-info":[{"award-number":["E317YR17","GK AD22035957","12273045","E016YR1R","E039SB1K"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["E317YR17","GK AD22035957","12273045","E016YR1R","E039SB1K"],"award-info":[{"award-number":["E317YR17","GK AD22035957","12273045","E016YR1R","E039SB1K"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Western Talent Introduction Project of Chinese Academy of Sciences","award":["E317YR17","GK AD22035957","12273045","E016YR1R","E039SB1K"],"award-info":[{"award-number":["E317YR17","GK AD22035957","12273045","E016YR1R","E039SB1K"]}]},{"name":"High Level Talent Project of Shaan xi Province","award":["E317YR17","GK AD22035957","12273045","E016YR1R","E039SB1K"],"award-info":[{"award-number":["E317YR17","GK AD22035957","12273045","E016YR1R","E039SB1K"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As a basic task and key link of space situational awareness, space target recognition has become crucial in threat analysis, communication reconnaissance and electronic countermeasures. Using the fingerprint features carried by the electromagnetic signal to recognize is an effective method. Because traditional radiation source recognition technologies are difficult to obtain satisfactory expert features, automatic feature extraction methods based on deep learning have become popular. Although many deep learning schemes have been proposed, most of them are only used to solve the inter-class separable problem and ignore the intra-class compactness. In addition, the openness of the real space may invalidate the existing closed-set recognition methods. In order to solve the above problems, inspired by the application of prototype learning in image recognition, we propose a novel method for recognizing space radiation sources based on a multi-scale residual prototype learning network (MSRPLNet). The method can be used for both the closed- and open-set recognition of space radiation sources. Furthermore, we also design a joint decision algorithm for an open-set recognition task to identify unknown radiation sources. To verify the effectiveness and reliability of the proposed method, we built a set of satellite signal observation and receiving systems in a real external environment and collected eight Iridium signals. The experimental results show that the accuracy of our proposed method can reach 98.34% and 91.04% for the closed- and open-set recognition of eight Iridium targets, respectively. Compared to similar research works, our method has obvious advantages.<\/jats:p>","DOI":"10.3390\/s23104708","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T09:56:18Z","timestamp":1683885378000},"page":"4708","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Method for Recognizing Space Radiation Sources Based on Multi-Scale Residual Prototype Learning Network"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5256-2705","authenticated-orcid":false,"given":"Pengfei","family":"Liu","sequence":"first","affiliation":[{"name":"National Time Service Center, Chinese Academy of Sciences, Xi\u2019an 710600, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Precise Positioning and Timing Technology, Chinese Academy of Sciences, Xi\u2019an 710600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lishu","family":"Guo","sequence":"additional","affiliation":[{"name":"National Time Service Center, Chinese Academy of Sciences, Xi\u2019an 710600, China"},{"name":"Key Laboratory of Precise Positioning and Timing Technology, Chinese Academy of Sciences, Xi\u2019an 710600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Time Service Center, Chinese Academy of Sciences, Xi\u2019an 710600, China"},{"name":"Key Laboratory of Precise Positioning and Timing Technology, Chinese Academy of Sciences, Xi\u2019an 710600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Shang","sequence":"additional","affiliation":[{"name":"National Time Service Center, Chinese Academy of Sciences, Xi\u2019an 710600, China"},{"name":"Key Laboratory of Precise Positioning and Timing Technology, Chinese Academy of Sciences, Xi\u2019an 710600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyue","family":"Chu","sequence":"additional","affiliation":[{"name":"National Time Service Center, Chinese Academy of Sciences, Xi\u2019an 710600, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Precise Positioning and Timing Technology, Chinese Academy of Sciences, Xi\u2019an 710600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaochun","family":"Lu","sequence":"additional","affiliation":[{"name":"National Time Service Center, Chinese Academy of Sciences, Xi\u2019an 710600, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Precise Positioning and Timing Technology, Chinese Academy of Sciences, Xi\u2019an 710600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107531","DOI":"10.1016\/j.patcog.2020.107531","article-title":"HCNN-PSI: A hybrid CNN with partial semantic information for space target recognition","volume":"108","author":"Yang","year":"2020","journal-title":"Pattern Recogn."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5113813","DOI":"10.1109\/TGRS.2022.3174677","article-title":"Smoothed Lv Distribution Based Three-Dimensional Imaging for Spinning Space Debris","volume":"60","author":"Zhuo","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1109\/TCOMM.2015.2401556","article-title":"Multicarrier Successive Predistortion for Nonlinear Satellite Systems","volume":"63","author":"Beidas","year":"2015","journal-title":"IEEE Trans. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"354","DOI":"10.23919\/JSEE.2022.000037","article-title":"A novel approach for unlabeled samples in radiation source identification","volume":"33","author":"Yang","year":"2022","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Restuccia, F., D\u2019Oro, S., Shawabka, A., Belgiovine, M., Angioloni, L., Loannidis, S., Chowdhury, K., and Melodia, T. (2019, January 2\u20135). DeepRadioID: Real-time channel-resilient optimization of deep learning-based radio fingerprinting algorithms. Proceedings of the 20th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Catania, Italy.","DOI":"10.1145\/3323679.3326503"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2591","DOI":"10.1109\/LCOMM.2018.2871465","article-title":"Specific Emitter Identification via Convolutional Neural Networks","volume":"22","author":"Ding","year":"2018","journal-title":"IEEE Commun. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wu, L., Zhao, Y., Wang, Z., Abdalla, F.Y.O., and Ren, G. (2017, January 18\u201320). Specific emitter identification using fractal features based on box-counting dimension and variance dimension. Proceedings of the 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Bilbao, Spain.","DOI":"10.1109\/ISSPIT.2017.8388646"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/TIFS.2016.2520908","article-title":"Specific Emitter Identification via Hilbert\u2013Huang Transform in Single-Hop and Relaying Scenarios","volume":"11","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2447","DOI":"10.1007\/s11277-017-4306-0","article-title":"Method of Empirical Mode Decomposition in Specific Emitter Identification","volume":"96","author":"Liang","year":"2017","journal-title":"Wireless. Pers. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"144054","DOI":"10.1109\/ACCESS.2019.2945121","article-title":"Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices","volume":"7","author":"Aghnaiya","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","first-page":"8000813","article-title":"RF Transmitter Identification Using Combined Siamese Networks","volume":"71","author":"Sun","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"204417","DOI":"10.1109\/ACCESS.2020.3037206","article-title":"Radio Frequency Fingerprint Identification Based on Deep Complex Residual Network","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MCOM.2018.1800153","article-title":"Deep Learning Convolutional Neural Networks for Radio Identification","volume":"56","author":"Riyaz","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/JRFID.2021.3051901","article-title":"Exploiting Spatio-Temporal Properties of I\/Q Signal Data Using 3D Convolution for RF Transmitter Identification","volume":"5","author":"Ramasubramanian","year":"2021","journal-title":"IEEE J. Radio. Freq. Identif."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1109\/JSTSP.2018.2796446","article-title":"Deep Learning for RF Device Fingerprinting in Cognitive Communication Networks","volume":"12","author":"Merchant","year":"2018","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1109\/JRFID.2018.2880457","article-title":"Machine Learning Approach to RF Transmitter Identification","volume":"2","author":"Youssef","year":"2018","journal-title":"IEEE J. Radio Freq. Identif."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, Y., Yu, L., Yao, Y., and Zhu, L. (2020, January 28\u201331). Individual Identification Technology of Communication Radiation Sources Based on Deep Learning. Proceedings of the 2020 IEEE 20th International Conference on Communication Technology (ICCT), Nanning, China.","DOI":"10.1109\/ICCT50939.2020.9295728"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Qu, L., Yang, J., Liu, H., Huang, K., Hu, P., Li, X., and Zhang, Y. (2021, January 18\u201320). Specific Emitter Identification Based on Two Residual Networks. Proceedings of the 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China.","DOI":"10.1109\/IMCEC51613.2021.9482046"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3222","DOI":"10.1109\/TNSE.2021.3103805","article-title":"Radio Frequency Fingerprint Collaborative Intelligent Identification Using Incremental Learning","volume":"9","author":"Liu","year":"2022","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"14797","DOI":"10.1109\/ACCESS.2022.3147980","article-title":"A Hybrid LSTM-ResNet Deep Neural Network for Noise Reduction and Classification of V-Band Receiver Signals","volume":"10","author":"Arab","year":"2022","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/TCCN.2021.3091730","article-title":"A Novel Automatic Modulation Classification Scheme Based on Multi-Scale Networks","volume":"8","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4445","DOI":"10.1109\/TGRS.2019.2891266","article-title":"Open Set Incremental Learning for Automatic Target Recognition","volume":"57","author":"Dang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","unstructured":"Dhamija, A.R., G\u00fcnther, M., and Boult, T.E. (2018). Reducing Network Agnostophobia. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4027305","DOI":"10.1109\/LGRS.2022.3211623","article-title":"Surrounding Prototype Loss for Radar HRRP Open Set Target Recognition","volume":"19","author":"Xia","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bendale, A., and Boult, T.E. (2016, January 27\u201330). Towards Open Set Deep Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.173"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/TCCN.2020.3043332","article-title":"Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations","volume":"7","author":"Hanna","year":"2021","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1109\/TCCN.2019.2948919","article-title":"RFAL: Adversarial Learning for RF Transmitter Identification and Classification","volume":"6","author":"Roy","year":"2020","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1109\/TCCN.2021.3118456","article-title":"Multi-Task Based Deep Learning Approach for Open-Set Wireless Signal Identification in ISM Band","volume":"8","author":"Gong","year":"2022","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108774","DOI":"10.1016\/j.apacoust.2022.108774","article-title":"Open set recognition of underwater acoustic targets based on GRU-CAE collaborative deep learning network","volume":"193","author":"Yang","year":"2022","journal-title":"Appl. Acoust."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108391","DOI":"10.1016\/j.sigpro.2021.108391","article-title":"Open set HRRP recognition with few samples based on multi-modality prototypical networks","volume":"193","author":"Tian","year":"2021","journal-title":"Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1109\/TPAMI.2014.2321392","article-title":"Probability Models for Open Set Recognition","volume":"36","author":"Scheirer","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3614","DOI":"10.1109\/TPAMI.2020.2981604","article-title":"Recent Advances in Open Set Recognition: A Survey","volume":"43","author":"Geng","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, H.M., Zhang, X.Y., Yin, F., and Liu, C.L. (2018, January 18\u201322). Robust Classification with Convolutional Prototype Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00366"},{"key":"ref_35","first-page":"2358","article-title":"Convolutional Prototype Network for Open Set Recognition","volume":"44","author":"Yang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cekic, M., Gopalakrishnan, S., and Madhow, U. (November, January 31). Wireless Fingerprinting via Deep Learning: The Impact of Confounding Factors. Proceedings of the 2021 55th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/IEEECONF53345.2021.9723393"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Qiang, Z., and Yan, T. (2022, January 23\u201324). ResNet Based Modulation Pattern Recognition Method in Low SNR. Proceedings of the 2022 IEEE 10th International Conference on Information, Communication and Networks (ICICN), Zhangye, China.","DOI":"10.1109\/ICICN56848.2022.10006602"},{"key":"ref_38","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":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4708\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:33:53Z","timestamp":1760124833000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4708"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,12]]},"references-count":38,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23104708"],"URL":"https:\/\/doi.org\/10.3390\/s23104708","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,5,12]]}}}