{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:44:02Z","timestamp":1772909042444,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"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>Radar emitter signal intra-pulse modulation recognition is important for modern electronic reconnaissance systems to analyze target radar systems. In the actual environment, the intra-pulse modulations of the sampled radar emitter signals contain not only the known types in the library but also the unknown types. Therefore, the existing recognition methods, which are based on a closed set, cannot recognize the unknown samples. In order to solve this problem, in this paper, we proposed a method for radar emitter signal intra-pulse modulation open set recognition. The proposed method could classify the known modulations and identify the unknown modulation by using an original deep neural network-based recognition model trained on a closed set, estimating the signal-to-noise ratio, and calculating the reconstruction loss by an encoder\u2013decoder model. For a given sample, the original deep neural network-based recognition model will label it as a certain known class temporarily. By estimating the SNR of the sample and calculating the reconstruction loss by inputting the sample to the corresponding encoder\u2013decoder model related to the temporary predicted known class, whether the sample belongs to the predicted temporary known class or the unknown class will be confirmed. Experiments were conducted with five different openness conditions. The experimental results indicate that the proposed method has good performance on radar emitter signal intra-pulse modulation open set recognition.<\/jats:p>","DOI":"10.3390\/rs16010108","type":"journal-article","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T02:58:12Z","timestamp":1703645892000},"page":"108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Radar Emitter Signal Intra-Pulse Modulation Open Set Recognition Based on Deep Neural Network"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7862-919X","authenticated-orcid":false,"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"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"key":"ref_1","unstructured":"Zhao, G. (2012). Principle of Radar Countermeasure, Xidian University Press. [2nd ed.]."},{"key":"ref_2","unstructured":"Richards, M.A. (2005). Fundamentals of Radar Signal Processing, McGraw-Hill Education. [2nd ed.]."},{"key":"ref_3","unstructured":"Barton, D.K. (2004). Radar System Analysis and Modeling, Artech."},{"key":"ref_4","unstructured":"Wiley, R.G., and Ebrary, I. (2006). ELINT: The Interception and Analysis of Radar Signals, Artech."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"43874","DOI":"10.1109\/ACCESS.2018.2864347","article-title":"Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network","volume":"6","author":"Qu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_6","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_7","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_8","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_9","doi-asserted-by":"crossref","first-page":"3305","DOI":"10.1109\/LCOMM.2021.3098050","article-title":"Intra-Pulse Modulation Recognition of Dual-Component Radar Signals Based on Deep Convolutional Neural Network","volume":"25","author":"Si","year":"2021","journal-title":"IEEE Commun. Lett."},{"key":"ref_10","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1049\/rsn2.12421","article-title":"Research on modulation recognition method of multi-component radar signals based on deep convolution neural network","volume":"17","author":"Wan","year":"2023","journal-title":"IET Radar Sonar Navig."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cai, J., He, M., Cao, X., and Gan, F. (2023). Semi-Supervised Radar Intra-Pulse Signal Modulation Classification With Virtual Adversarial Training. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2023.3325943"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yuan, S., Li, P., Wu, B., Li, X., and Wang, J. (2022). Semi-Supervised Classification for Intra-Pulse Modulation of Radar Emitter Signals Using Convolutional Neural Network. Remote Sens., 14.","DOI":"10.3390\/rs14092059"},{"key":"ref_14","unstructured":"Hendrycks, D., and Gimpel, K. (2016). A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bendale, A., and Boult, T.E. (2016, January 27\u201330). Towards Open Set Deep Networks. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.173"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sun, C., Du, Y., Qiao, X., Wu, H., and Zhang, T. (2023). Research on the Enhancement Method of Specific Emitter Open Set Recognition. Electronics, 12.","DOI":"10.3390\/electronics12214399"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Shang, S., Song, X., Zhang, S., You, T., and Zhang, L. (2022). Intelligent Radar Jamming Recognition in Open Set Environment Based on Deep Learning Networks. Remote Sens., 14.","DOI":"10.3390\/rs14246220"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1109\/78.382394","article-title":"Improving the readability of time-frequency and time-scale representations by the reassignment method","volume":"43","author":"Auger","year":"1995","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, X., Huang, G., Zhou, Z., Tian, W., Yao, J., and Gao, J. (2018). Radar Emitter Recognition Based on the Energy Cumulant of Short Time Fourier Transform and Reinforced Deep Belief Network. Sensors, 18.","DOI":"10.3390\/s18093103"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3516","DOI":"10.1109\/TSP.2019.2918983","article-title":"Automatic Recognition of General LPI Radar Waveform Using SSD and Supplementary Classifier","volume":"67","author":"Hoang","year":"2019","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_21","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_22","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_23","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_24","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","unstructured":"Vaze, S., Han, K., Vedaldi, A., and Zisserman, A. (2021). Open-set recognition: A good closed-set classifier is all you need. arXiv."},{"key":"ref_28","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.-W., and Wu, J. (2020, January 4\u20138). UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5107511","DOI":"10.1109\/TGRS.2021.3129645","article-title":"Radar Deception Jamming Recognition Based on Weighted Ensemble CNN With Transfer Learning","volume":"60","author":"Lv","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhou, X., Bai, X., and Xue, R. (2021, January 15\u201319). ISAR Target Recognition Based on Capsule Net. Proceedings of the 2021 CIE International Conference on Radar (Radar), Haikou, China.","DOI":"10.1109\/Radar53847.2021.10028247"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5795","DOI":"10.1109\/TSP.2020.3027470","article-title":"Variational Temporal Deep Generative Model for Radar HRRP Target Recognition","volume":"68","author":"Guo","year":"2020","journal-title":"IEEE Trans. Signal Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/108\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:42:22Z","timestamp":1760132542000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/108"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16010108"],"URL":"https:\/\/doi.org\/10.3390\/rs16010108","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,26]]}}}