{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T20:57:17Z","timestamp":1773435437639,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Intra-pulse modulation classification of radar emitter signals is beneficial in analyzing radar systems. Recently, convolutional neural networks (CNNs) have been used in classification of intra-pulse modulation of radar emitter signals, and the results proved better than the traditional methods. However, there is a key disadvantage in these CNN-based methods: the CNN requires enough labeled samples. Labeling the modulations of radar emitter signal samples requires a tremendous amount of prior knowledge and human resources. In many circumstances, the labeled samples are quite limited compared with the unlabeled samples, which means that the classification will be semi-supervised. In this paper, we propose a method which could adapt the CNN-based intra-pulse classification approach to the case where a very limited number of labeled samples and a large number of unlabeled samples are provided, to classify the intra-pulse modulations of radar emitter signals. The method is based on a one-dimensional CNN and uses pseudo labels and self-paced data augmentation, which could improve the accuracy of intra-pulse classification. Extensive experiments show that our proposed method can improve the intra-pulse modulation classification performance in the semi-supervised situations.<\/jats:p>","DOI":"10.3390\/rs14092059","type":"journal-article","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T02:14:39Z","timestamp":1650939279000},"page":"2059","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Semi-Supervised Classification for Intra-Pulse Modulation of Radar Emitter Signals Using Convolutional Neural Network"],"prefix":"10.3390","volume":"14","author":[{"given":"Shibo","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Peng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2755-6706","authenticated-orcid":false,"given":"Bin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Xiao","family":"Li","sequence":"additional","affiliation":[{"name":"Southwest China Research Institute of Electronic Equipment, Chengdu 610036, China"}]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"Southwest China Research Institute of Electronic Equipment, Chengdu 610036, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"ref_1","unstructured":"Barton, D.K. (2004). Radar System Analysis and Modeling, Artech."},{"key":"ref_2","unstructured":"Richards, M.A. (2005). Fundamentals of Radar Signal Processing, McGraw-Hill Education. [2nd ed.]."},{"key":"ref_3","unstructured":"Wiley, R.G., and Ebrary, I. (2006). ELINT: The Interception and Analysis of Radar Signals, Artech."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"49125","DOI":"10.1109\/ACCESS.2020.2980363","article-title":"Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network and Deep Q-Learning Network","volume":"8","author":"Qu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_6","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). ImageNet Classification with Deep Convolutional Neural Networks, NIPS Curran Associates Inc."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_8","unstructured":"Srivastava, R.K., Greff, K., and Schmidhuber, J. (2015). Highway networks. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., and Yang, J. (2019, January 15\u201320). Selective Kernel Networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9257","DOI":"10.1109\/TGRS.2021.3051024","article-title":"Hybrid Inference Network for Few-Shot SAR Automatic Target Recognition","volume":"59","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4207","DOI":"10.1109\/ACCESS.2017.2788942","article-title":"Automatic LPI Radar Waveform Recognition Using CNN","volume":"6","author":"Kong","year":"2018","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yu, Z., and Tang, J. (October, January 26). Radar Signal Intra-Pulse Modulation Recognition Based on Contour Extraction. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324209"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1049\/iet-rsn.2017.0265","article-title":"Modulation classification method for frequency modulation signals based on the time\u2013frequency distribution and CNN","volume":"12","author":"Zhang","year":"2017","journal-title":"IET Radar Sonar Navigat."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1109\/LCOMM.2020.2968030","article-title":"MCNet: An Efficient CNN Architecture for Robust Automatic Modulation Classification","volume":"24","author":"Hua","year":"2020","journal-title":"IEEE Commun. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/TNNLS.2018.2850703","article-title":"Modulation Classification Based on Signal Constellation Diagrams and Deep Learning","volume":"30","author":"Peng","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/LCOMM.2021.3070151","article-title":"GCPS: A CNN Performance Evaluation Criterion for Radar Signal Intrapulse Modulation Recognition","volume":"25","author":"Yu","year":"2021","journal-title":"IEEE Commun. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, B., Yuan, S., Li, P., Jing, Z., Huang, S., and Zhao, Y. (2020). Radar Emitter Signal Recognition Based on One-Dimensional Convolutional Neural Network with Attention Mechanism. Sensors, 20.","DOI":"10.3390\/s20216350"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yuan, S., Wu, B., and Li, P. (2021). Intra-Pulse Modulation Classification of Radar Emitter Signals Based on a 1-D Selective Kernel Convolutional Neural Network. Remote Sens., 13.","DOI":"10.3390\/rs13142799"},{"key":"ref_21","first-page":"2","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume":"Volume 3","author":"Lee","year":"2013","journal-title":"Workshop on Challenges in Representation Learning"},{"key":"ref_22","unstructured":"Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., and Raffel, C.A. (2019). Mixmatch: A holistic approach to semi-supervised learning. arXiv."},{"key":"ref_23","unstructured":"Sohn, K., Berthelot, D., Li, C.L., Zhang, Z., Carlini, N., Cubuk, E.D., Kurakin, A., Zhang, H., and Raffel, C. (2020). FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. arXiv."},{"key":"ref_24","unstructured":"Bachman, P., Alsharif, O., and Precup, D. (2014). Learning with pseudo-ensembles. Adv. Neural Inf. Process. Syst., 3365\u20133373."},{"key":"ref_25","unstructured":"Sajjadi, M., Javanmardi, M., and Tasdizen, T. (2016). Regularization with stochastic transformations and perturbations for deep semi-supervised learning. Adv. Neural Inf. Process. Syst., 1171\u20131179."},{"key":"ref_26","unstructured":"Laine, S., and Aila, T. (2016). Temporal ensembling for semi-supervised learning. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., and Chua, T.-S. (2017, January 21\u201326). SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.667"},{"key":"ref_29","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_30","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_31","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2059\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:00:51Z","timestamp":1760137251000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2059"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":31,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092059"],"URL":"https:\/\/doi.org\/10.3390\/rs14092059","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,25]]}}}