{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:34:39Z","timestamp":1759970079422,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Communication radiation source individual identification technology is an essential technique in electronic reconnaissance and a crucial link in electronic warfare support measures. Nevertheless, when the sample set encounters complex circumstances, such as class imbalance or a small sample, the classification network model, driven by big data, disrupts the symmetry between the recognition effect and the quantity of the datasets, leading to suboptimal recognition performance. Thus, it is requisite to optimize the existing models and algorithms to better propose more representative fingerprint features. This paper references the speech signal recognition model multivariate long short-term memory\u2013fully convolutional network (MLSTM-FCN), and ameliorates the recognition algorithm and training strategy for the two scenarios of class imbalance and a small sample. It puts forward a communication radiation source individual identification method based on MLSTM-FCN incremental random feature concatenation and a communication radiation source individual identification method based on meta-learning. Proceeding from improving the class imbalance issue among features and small-sample learning, the experimental results under various signal-to-noise ratios demonstrate that the proposed methods have superior recognition effects and higher accuracy.<\/jats:p>","DOI":"10.3390\/sym17010097","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T11:29:37Z","timestamp":1736422177000},"page":"97","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on the Individual Identification of Communication Radiation Sources in Complex Circumstances"],"prefix":"10.3390","volume":"17","author":[{"given":"Yameng","family":"Niu","sequence":"first","affiliation":[{"name":"College of Electronic Engineering, Naval University of Engineering, Wuhan 430000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangzhong","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, Naval University of Engineering, Wuhan 430000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiping","family":"Liu","sequence":"additional","affiliation":[{"name":"The Technology Innovation Center, Naval University of Engineering, Wuhan 430000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,9]]},"reference":[{"key":"ref_1","first-page":"108","article-title":"Harnessing AI for Information Dominance in Modern Warfare","volume":"6","author":"Ret","year":"2024","journal-title":"Mar. Corps Gaz."},{"key":"ref_2","unstructured":"Yao, Y. (2020). Research on Nonlinear Characteristics of Radio Frequency Transmitter in Wireless Communication. [Master\u2019s Thesis, University of Electronic Science and Technology of China]."},{"key":"ref_3","unstructured":"(2020). Andy. Research on Signal and Radiation Source Association Identification Based on Machine Learning. [Master\u2019s Thesis, Xidian University]."},{"key":"ref_4","unstructured":"Chen, T. (2023). Research on the Development and Application of Contemporary Military Technology. [Master\u2019s Thesis, National University of Defense Technology]."},{"key":"ref_5","first-page":"113","article-title":"Specific emitter identification and verification","volume":"113","author":"Talbot","year":"2003","journal-title":"Technol. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5889","DOI":"10.1109\/TWC.2015.2443794","article-title":"Identification of Wireless Devices of Users Who Actively Fake Their RF Fingerprints with Artificial Data Distortion","volume":"14","author":"Polak","year":"2015","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"26439","DOI":"10.1007\/s11042-023-16549-6","article-title":"Global aspects and overview of 5G multimedia communication","volume":"83","author":"Devrari","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_8","first-page":"798","article-title":"Communication radiation source individual recognition method in response to receiver change","volume":"35","author":"Bai","year":"2023","journal-title":"J. Chongqing Univ. Posts Telecommun. (Nat. Sci. Ed.)"},{"key":"ref_9","first-page":"99","article-title":"Individual identification of communication radiation source based on 3D-Hibert energy spectrum and multi-scale fractal characteristics","volume":"38","author":"Han","year":"2017","journal-title":"J. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yue, J., Gao, L., and Zheng, N. (2019, January 20\u201322). Research on C-E Fingerprint Extraction of Emitter. Proceedings of the 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chengdu, China.","DOI":"10.1109\/IAEAC47372.2019.8998027"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1007\/s11036-020-01613-4","article-title":"Research on Fingerprint Identification of Wireless Devices Based on Information Fusion","volume":"25","author":"Tian","year":"2020","journal-title":"Mob. Netw. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"122870.1","DOI":"10.1016\/j.eswa.2023.122870","article-title":"Unleashing the full potential of hyperspectral imaging: Decoupled image and frequency-domain spatial-spectral framework","volume":"243","author":"He","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Guo, S., Xu, Y., Huang, W., and Liu, B. (2021, January 1\u20133). Specific Emitter Identification via Variational Mode Decomposition and Histogram of Oriented Gradient. Proceedings of the International Conference on Telecommunications, London, UK.","DOI":"10.1109\/ICT52184.2021.9511516"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.procs.2024.03.046","article-title":"Classification of Congestive Heart Failure Using Artificial Neural Network Based on Higher-Order Moments Detrended Fluctuation Analysis of Heart Rate Variability","volume":"234","author":"Mahananto","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"54425","DOI":"10.1109\/ACCESS.2019.2913759","article-title":"Specific Emitter Identification Based on Deep Residual Networks","volume":"7","author":"Pan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_16","first-page":"290","article-title":"Radar radiation source individual recognition based on attention mechanism and CNN","volume":"44","author":"Yang","year":"2023","journal-title":"J. Ordnance Equip. Eng."},{"key":"ref_17","first-page":"186","article-title":"Individual identification of semi-supervised unknown communication radiation source based on Convolutional neural network","volume":"9","author":"Bai","year":"2023","journal-title":"Inf. Technol. Informatiz."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zha, X., Qin, X., Zhou, Y., and Peng, H. (2019, January 24\u201326). Power of Deep Learning for Amplitude-phase Signal Modulation Recognition. Proceedings of the 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.","DOI":"10.1109\/ITAIC.2019.8785607"},{"key":"ref_19","first-page":"1","article-title":"Individual recognition of ADS-B radiation source based on deep neural network and Stochastic forest integrated model","volume":"42","author":"Wang","year":"2023","journal-title":"Foreign Electron. Meas. Technol."},{"key":"ref_20","unstructured":"Liang, P. (2023). Research on Lightweight Multi-Mode Radiation Source Individual Recognition Based on Deep Learning. [Master\u2019s Thesis, University of Electronic Science and Technology of China]."},{"key":"ref_21","first-page":"71","article-title":"Communication radiation source individual identification based on deep adaptive wavelet network","volume":"42","author":"Liu","year":"2023","journal-title":"Netw. Secur. Data Gov."},{"key":"ref_22","first-page":"95","article-title":"A method for individual identification of communication radiation sources based on complex residual networks","volume":"37","author":"Qu","year":"2021","journal-title":"Signal Process."},{"key":"ref_23","unstructured":"Huang, J. (2020). Research on Communication Radiation Source Individual Recognition Technology Based on Deep Learning. [Master\u2019s Thesis, National University of Defense Technology]."},{"key":"ref_24","unstructured":"Yu, W. (2024). Research on Deep Learning Method for Individual Recognition of Wi-Fi Signal Radiation Source. [Master\u2019s Thesis, Xi\u2019an University of Technology]."},{"key":"ref_25","first-page":"13","article-title":"Network Traffic anomaly detection Algorithm based on Multi-feature Extraction autoencoder","volume":"12","author":"Qin","year":"2023","journal-title":"China Cable Telev."},{"key":"ref_26","first-page":"318","article-title":"A class-oriented unbalance SSL VPN encryption traffic identification method","volume":"40","author":"Wang","year":"2023","journal-title":"Comput. Appl. Softw."},{"key":"ref_27","first-page":"80","article-title":"Random forest unbalanced data classification algorithm based on DBSCAN cluster decomposition and oversampling","volume":"49","author":"Zhao","year":"2023","journal-title":"J. Lanzhou Univ. Technol."},{"key":"ref_28","first-page":"257","article-title":"Prediction Method of Learning Results Based on Improved SMOTE Algorithm and Ensemble Model","volume":"3","author":"Wang","year":"2024","journal-title":"J. North Univ. China (Nat. Sci. Ed.)"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Yan, T. (2021, January 4\u20137). Radio Frequency Fingerprint Recognition Method Based on Generative Adversarial Net. Proceedings of the 2021 13th International Conference on Communication Software and Networks (ICCSN), Chongqing, China.","DOI":"10.1109\/ICCSN52437.2021.9463629"},{"key":"ref_30","first-page":"35","article-title":"Small sample radiation source individual recognition method based on meta-metric learning in complex domain","volume":"24","author":"Chen","year":"2023","journal-title":"J. Univ. Inf. Eng."},{"key":"ref_31","unstructured":"Chen, J. (2023). Research on Communication Radiation Source Individual Recognition Technology Based on Deep Learning. [Master\u2019s Thesis, Zhengzhou University]."},{"key":"ref_32","first-page":"38","article-title":"Small sample learning based on improved relational network","volume":"44","author":"Wang","year":"2020","journal-title":"J. Anhui Univ. (Nat. Sci.)"},{"key":"ref_33","first-page":"1666","article-title":"Small sample OFDM target enhancement recognition method based on transfer learning","volume":"56","author":"Tang","year":"2022","journal-title":"J. Shanghai Jiao Tong Univ."},{"key":"ref_34","unstructured":"Wang, Y., Chao, W.L., Weinberger, K.Q., and van der Maaten, L. (2019). SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, J., Song, L., and Qin, Y. (2020, January 23\u201328). Prototype Rectification for Few-Shot Learning. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_43"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.neunet.2019.04.014","article-title":"Multivariate LSTM-FCNs for time series classification","volume":"116","author":"Karim","year":"2019","journal-title":"Neural Networks  Off. J. Int. Neural Netw. Soc."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W., and Oates, T. (2017, January 14\u201319). Time series classification from scratch with deep neural networks: A strong baseline. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966039"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/1\/97\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:26:02Z","timestamp":1759919162000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/1\/97"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,9]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["sym17010097"],"URL":"https:\/\/doi.org\/10.3390\/sym17010097","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,1,9]]}}}