{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:20:24Z","timestamp":1773246024022,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T00:00:00Z","timestamp":1646352000000},"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":["61973283"],"award-info":[{"award-number":["61973283"]}],"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>Accurate recognition of radar modulation mode helps to better estimate radar echo parameters, thereby occupying an advantageous position in the radar electronic warfare (EW). However, under low signal-to-noise ratio environments, recent deep-learning-based radar signal recognition methods often perform poorly due to the unsuitable denoising preprocess. In this paper, a denoising-guided disentangled network based on an inception structure is proposed to simultaneously complete the denoising and recognition of radar signals in an end-to-end manner. The pure radar signal representation (PSR) is disentangled from the noise signal representation (NSR) through a feature disentangler and used to learn a radar signal modulation recognizer under low-SNR environments. Signal noise mutual information loss is proposed to enlarge the gap between the PSR and the NSR. Experimental results demonstrate that our method can obtain a recognition accuracy of 98.75% in the \u22128 dB SNR and 89.25% in the \u221210 dB environment of 12 modulation formats.<\/jats:p>","DOI":"10.3390\/rs14051252","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T20:40:02Z","timestamp":1646599202000},"page":"1252","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Radar Signal Intrapulse Modulation Recognition Based on a Denoising-Guided Disentangled Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6112-9444","authenticated-orcid":false,"given":"Xiangli","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Jiazhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China"},{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Tianze","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Tianye","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zuping","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Electronic and Communication, Huazhong Universtiy of Science and Technology, Wuhan 430074, China"}]},{"given":"Ying","family":"Chen","sequence":"additional","affiliation":[{"name":"Intelligent Technology Co., Ltd., Chinese Construction Third Engineering Bureau, Wuhan 430074, China"}]},{"given":"Jinsheng","family":"Li","sequence":"additional","affiliation":[{"name":"Intelligent Technology Co., Ltd., Chinese Construction Third Engineering Bureau, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1538-2617","authenticated-orcid":false,"given":"Dapeng","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zuo, L., Wang, J., Sui, J., and Li, N. (2021). An Inter-Subband Processing Algorithm for Complex Clutter Suppression in Passive Bistatic Radar. Remote Sens., 13.","DOI":"10.3390\/rs13234954"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhang, J., and Sun, W. (2021). Recognition of The Typical Distress in Concrete Pavement Based on GPR and 1D-CNN. Remote Sens., 13.","DOI":"10.3390\/rs13122375"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107393","DOI":"10.1016\/j.sigpro.2019.107393","article-title":"Automatic Modulation Recognition of Compound Signals Using a Deep Multilabel Classifier: A Case Study with Radar Jamming Signals","volume":"169","author":"Zhu","year":"2020","journal-title":"Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1109\/TAES.2017.2667142","article-title":"Automatic Intrapulse Modulation Classification of Advanced LPI Radar Waveforms","volume":"53","author":"Rao","year":"2017","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1109\/LWC.2018.2867459","article-title":"Adversarial Attacks on Deep Learning-based Radio Signal Classification","volume":"8","author":"Sadeghi","year":"2019","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3587","DOI":"10.1109\/TWC.2021.3052222","article-title":"Multi-Task Learning for Generalized Automatic Modulation Classification under Non-Gaussian Noise with Varying SNR Conditions","volume":"20","author":"Wang","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1109\/TWC.2011.122211.110236","article-title":"Blind Digital Modulation Identification for Spatially Correlated MIMO Systems","volume":"11","author":"Hassan","year":"2012","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5688","DOI":"10.1109\/TVT.2020.2981995","article-title":"Automatic Modulation Classification for MIMO Systems via Deep Learning and Zero-Forcing Equalization","volume":"69","author":"Wang","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1626","DOI":"10.1109\/LSP.2017.2752459","article-title":"Automatic Modulation Classification Using Deep Learning Based on Sparse Autoencoders with Nonnegativity Constraints","volume":"24","author":"Ali","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.neunet.2019.12.024","article-title":"Attention-guided CNN for Image Denoising","volume":"124","author":"Tian","year":"2020","journal-title":"Neural Netw."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"112339","DOI":"10.1109\/ACCESS.2019.2935247","article-title":"Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Denoising Autoencoder and Deep Convolutional Neural Network","volume":"7","author":"Qu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/0165-1684(95)00099-2","article-title":"Automatic Identification of Digital Modulation Types","volume":"47","author":"Azzouz","year":"1995","journal-title":"Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, L., Yang, Z., and Lu, W. (2020, January 23\u201325). Digital Modulation Classification Based on Higher-order Moments and Characteristic Function. Proceedings of the 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP), Nanjing, China.","DOI":"10.1109\/ICSIP49896.2020.9339255"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1049\/iet-spr.2011.0357","article-title":"Multiuser Modulation Classification Based on Cumulants in Additive White Gaussian Noise Channel","volume":"6","author":"Zaerin","year":"2012","journal-title":"IET Signal Process."},{"key":"ref_18","unstructured":"Lunden, J., Terho, L., and Koivunen, V. (2005, January 28). Waveform Recognition in Pulse Compression Radar Systems. Proceedings of the 2005 IEEE Workshop on Machine Learning for Signal Processing, Mystic, CT, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1109\/TGRS.2013.2241775","article-title":"The Autocorrelation Spectral Density for Doppler-Weather-Radar Signal Analysis","volume":"52","author":"Warde","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","unstructured":"Shi, Z., Wu, H., Shen, W., Cheng, S., and Chen, Y. (2016, January 3\u20135). Feature Extraction for Complicated Radar PRI Modulation Modes Based on Auto-correlation Function. Proceedings of the 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi\u2019an, China."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gulum, T.O., Erdogan, A.Y., Yildirim, T., and Pace, P.E. (2012, January 7\u201311). A Parameter Extraction Technique for FMCW Radar Signals Using Wigner-Hough-Radon Transform. Proceedings of the 2012 IEEE National Radar Conference, Atlanta, GA, USA.","DOI":"10.1109\/RADAR.2012.6212255"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1109\/TAES.2013.6494422","article-title":"Adaptive Distributed MIMO Radar Waveform Optimization Based on Mutual Information","volume":"49","author":"Chen","year":"2013","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wu, A., Han, Y., Zhu, L., and Yang, Y. (IEEE Trans. Pattern Anal. Mach. Intell., 2021). Instance-Invariant Domain Adaptive Object Detection via Progressive Disentanglement, IEEE Trans. Pattern Anal. Mach. Intell., in press.","DOI":"10.1109\/TPAMI.2021.3060446"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"43874","DOI":"10.1109\/ACCESS.2018.2864347","article-title":"Radar Signal Intrapulse Modulation Recognition Based on Convolutional Neural Network","volume":"6","author":"Qu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, H., Zhang, F., Tang, B., Yin, Q., and Sun, X. (2018). Slim and Efficient Neural Network Design for Resource-Constrained SAR Target Recognition. Remote Sens., 10.","DOI":"10.3390\/rs10101618"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jan, M., and Pietrow, D. (2020, January 25\u201329). Artificial Neural Networks in The Filtration of Radiolocation Information. Proceedings of the 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), Lviv-Slavske, Ukraine.","DOI":"10.1109\/TCSET49122.2020.235518"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Deng, W., Zhao, L., Liao, Q., Guo, D., Kuang, G., Hu, D., and Liu, L. (IEEE Trans. Multimed., 2021). Informative Feature Disentanglement for Unsupervised Domain Adaptation, IEEE Trans. Multimed., in press.","DOI":"10.1109\/TMM.2021.3080516"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1109\/LSP.2020.3020215","article-title":"Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction","volume":"27","author":"Han","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector network","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1252\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:31:51Z","timestamp":1760135511000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1252"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,4]]},"references-count":30,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14051252"],"URL":"https:\/\/doi.org\/10.3390\/rs14051252","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,4]]}}}