{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T07:21:51Z","timestamp":1783840911737,"version":"3.55.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T00:00:00Z","timestamp":1599696000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T00:00:00Z","timestamp":1599696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671168"],"award-info":[{"award-number":["61671168"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801143"],"award-info":[{"award-number":["61801143"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005046","name":"National Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"crossref","award":["QC2016085"],"award-info":[{"award-number":["QC2016085"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["HEUCFJ180801"],"award-info":[{"award-number":["HEUCFJ180801"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1007\/s11042-020-09490-5","type":"journal-article","created":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T10:23:52Z","timestamp":1599733432000},"page":"1779-1792","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Towards an accurate radar waveform recognition algorithm based on dense CNN"],"prefix":"10.1007","volume":"80","author":[{"given":"Weijian","family":"Si","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4853-375X","authenticated-orcid":false,"given":"Chenxia","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunjie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,9,10]]},"reference":[{"issue":"4","key":"9490_CR1","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1049\/iet-rsn.2017.0354","volume":"12","author":"S Ayazgok","year":"2018","unstructured":"Ayazgok S, Erdem C, Ozturk MT, Orduyilmaz A, Serin M (2018) Automatic antenna scan type classification for next-generation electronic warfare receivers. IET Radar Sonar Navig 12(4):466\u2013474. https:\/\/doi.org\/10.1049\/iet-rsn.2017.0354","journal-title":"IET Radar Sonar Navig"},{"issue":"20","key":"9490_CR2","doi-asserted-by":"publisher","first-page":"28953","DOI":"10.1007\/s11042-018-6134-y","volume":"78","author":"R Cao","year":"2019","unstructured":"Cao R, Cao JW, Mei JP, Yin C, Huang XG (2019) Radar emitter identification with bispectrum and hierarchical extreme learning machine. Multimed Tools Appl 78(20):28953\u201328970. https:\/\/doi.org\/10.1007\/s11042-018-6134-y","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"9490_CR3","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1117\/1.jei.29.1.013003","volume":"29","author":"H Chen","year":"2020","unstructured":"Chen H, Yin JJ, Yeh CM, Lu YB, Yang JY (2020) Inverse synthetic aperture radar imaging based on time-frequency analysis through neural network. J Electron Imaging 29(1):20. https:\/\/doi.org\/10.1117\/1.jei.29.1.013003","journal-title":"J Electron Imaging"},{"issue":"4","key":"9490_CR4","doi-asserted-by":"publisher","first-page":"463","DOI":"10.3390\/electronics8040463","volume":"8","author":"J Gao","year":"2019","unstructured":"Gao J, Shen L, Gao L, Lu Y (2019) A rapid accurate recognition system for radar emitter signals. Electronics 8(4):463. https:\/\/doi.org\/10.3390\/electronics8040463","journal-title":"Electronics"},{"key":"9490_CR5","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Int. conf. on artificial intelligence and statistics, Fort Lauderdale, USA. pp. 315\u2013323"},{"issue":"4","key":"9490_CR6","doi-asserted-by":"publisher","first-page":"540","DOI":"10.3390\/sym11040540","volume":"11","author":"Q Guo","year":"2019","unstructured":"Guo Q, Yu X, Ruan G (2019) LPI radar waveform recognition based on deep convolutional neural network transfer learning. Symmetry-Basel 11(4):540. https:\/\/doi.org\/10.3390\/sym11040540","journal-title":"Symmetry-Basel"},{"key":"9490_CR7","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: computer vision and pattern recognition, Nevada, USA. pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9490_CR8","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: IEEE conf. on computer vision and pattern recognition, Honolulu, USA, pp. 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"9490_CR9","doi-asserted-by":"publisher","first-page":"98653","DOI":"10.1109\/access.2019.2930250","volume":"7","author":"Z Huang","year":"2019","unstructured":"Huang Z, Ma Z, Huang G (2019) Radar waveform recognition based on multiple autocorrelation images. IEEE Access 7:98653\u201398668. https:\/\/doi.org\/10.1109\/access.2019.2930250","journal-title":"IEEE Access"},{"key":"9490_CR10","unstructured":"Ioffe S, Szegedy CJapa (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Int. Conf. on Machine Learning, Lille, France. pp. 1\u201311"},{"issue":"6","key":"9490_CR11","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1109\/MAES.2004.1308837","volume":"19","author":"ELJIA Key","year":"2004","unstructured":"Key ELJIA, Magazine ES (2004) Detecting and classifying low probability of intercept radar [book review]. IEEE Aerosp Electron Syst Mag 19(6):39\u201341. https:\/\/doi.org\/10.1109\/MAES.2004.1308837","journal-title":"IEEE Aerosp Electron Syst Mag"},{"key":"9490_CR12","unstructured":"Kingma DP, Ba J Adam (2015): A method for stochastic optimization. In: Int. Conf. for Learning Representations, San Diego, USA. pp. 1\u201315"},{"issue":"2","key":"9490_CR13","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1109\/taes.2017.2667142","volume":"53","author":"TR Kishore","year":"2017","unstructured":"Kishore TR, Rao KD (2017) Automatic intrapulse modulation classification of advanced LPI radar waveforms. IEEE Trans Aerosp Electron Syst 53(2):901\u2013914. https:\/\/doi.org\/10.1109\/taes.2017.2667142","journal-title":"IEEE Trans Aerosp Electron Syst"},{"key":"9490_CR14","doi-asserted-by":"publisher","first-page":"4207","DOI":"10.1109\/access.2017.2788942","volume":"6","author":"S-H Kong","year":"2018","unstructured":"Kong S-H, Kim M, Linh Manh H, Kim E (2018) Automatic LPI radar wave form recognition using CNN. IEEE Access 6:4207\u20134219. https:\/\/doi.org\/10.1109\/access.2017.2788942","journal-title":"IEEE Access"},{"issue":"13","key":"9490_CR15","doi-asserted-by":"publisher","first-page":"3516","DOI":"10.1109\/tsp.2019.2918983","volume":"67","author":"H Linh Manh","year":"2019","unstructured":"Linh Manh H, Kim M, Kong S-H (2019) Automatic recognition of general LPI radar waveform using SSD and supplementary classifier. IEEE Trans Signal Process 67(13):3516\u20133530. https:\/\/doi.org\/10.1109\/tsp.2019.2918983","journal-title":"IEEE Trans Signal Process"},{"issue":"4","key":"9490_CR16","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1109\/taes.2018.2874139","volume":"55","author":"Z-M Liu","year":"2019","unstructured":"Liu Z-M, Yu PS (2019) Classification, denoising, and deinterleaving of pulse streams with recurrent neural networks. IEEE Trans Aeros Electron Syst 55(4):1624\u20131639. https:\/\/doi.org\/10.1109\/taes.2018.2874139","journal-title":"IEEE Trans Aeros Electron Syst"},{"issue":"5","key":"9490_CR17","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1109\/JSEE.2015.00106","volume":"26","author":"Y Liu","year":"2015","unstructured":"Liu Y, Xiao P, Wu H, Xiao W (2015) LPI radar signal detection based on radial integration of Choi-Williams time-frequency image. J Sys Eng Electron 26(5):973\u2013981","journal-title":"J Sys Eng Electron"},{"key":"9490_CR18","doi-asserted-by":"crossref","unstructured":"Lu X, Ma C, Ni B, Yang X, Reid I, Yang M (2018) Deep regression tracking with shrinkage loss. In: 15th European conference on computer vision, Munich, Germany. pp. 369\u2013386","DOI":"10.1007\/978-3-030-01264-9_22"},{"key":"9490_CR19","doi-asserted-by":"crossref","unstructured":"Lu X, Wang W, Ma C, Shen J, Shao L, Porikli F (2019) See more, know more: unsupervised video object segmentation with co-attention siamese networks. In: computer vision and pattern recognition, Long Beach, USA. pp. 3623\u20133632","DOI":"10.1109\/CVPR.2019.00374"},{"issue":"1","key":"9490_CR20","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1109\/jstsp.2007.897055","volume":"1","author":"J Lund\u00e9n","year":"2007","unstructured":"Lund\u00e9n J, Koivunen V (2007) Automatic radar waveform recognition. IEEE J Sel Top Signal Process 1(1):124\u2013136. https:\/\/doi.org\/10.1109\/jstsp.2007.897055","journal-title":"IEEE J Sel Top Signal Process"},{"issue":"12","key":"9490_CR21","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.3390\/electronics8121419","volume":"8","author":"Z Ma","year":"2019","unstructured":"Ma Z, Huang Z, Lin A, Huang G (2019) Emitter signal waveform classification based on autocorrelation and time-frequency analysis. Electronics 8(12):1419. https:\/\/doi.org\/10.3390\/electronics8121419","journal-title":"Electronics"},{"key":"9490_CR22","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/s11042-020-08657-4","volume":"4","author":"A Noor","year":"2020","unstructured":"Noor A, Zhao YQ, Khan R, Wu LW, Abdalla FYO (2020) Median filters combined with denoising convolutional neural network for Gaussian and impulse noises. Multimed Tools Appl 4:16\u201318568. https:\/\/doi.org\/10.1007\/s11042-020-08657-4","journal-title":"Multimed Tools Appl"},{"issue":"10","key":"9490_CR23","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359. https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"9490_CR24","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/TCCN.2019.2949279","volume":"6","author":"Z Qin","year":"2019","unstructured":"Qin Z, Zhou X, Zhang L, Gao Y, Liang Y-C, Li GY (2019) 20 years of evolution from cognitive to intelligent communications. IEEE Trans Cognitive Commun Netw 6(1):6\u201320. https:\/\/doi.org\/10.1109\/TCCN.2019.2949279","journal-title":"IEEE Trans Cognitive Commun Netw"},{"key":"9490_CR25","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: computer vision and pattern recognition, Columbus, USA. pp. 1\u201314"},{"key":"9490_CR26","unstructured":"Srivastava RK, Greff K, Schmidhuber J (2015) Training very deep networks. In: 28th international conference on neural information processing systems, Montrea, Canada. pp. 2377\u20132385"},{"issue":"5","key":"9490_CR27","doi-asserted-by":"publisher","first-page":"725","DOI":"10.3390\/sym11050725","volume":"11","author":"J Wan","year":"2019","unstructured":"Wan J, Yu X, Guo Q (2019) LPI radar waveform recognition based on CNN and TPOT. Symmetry-Basel 11(5):725. https:\/\/doi.org\/10.3390\/sym11050725","journal-title":"Symmetry-Basel"},{"key":"9490_CR28","doi-asserted-by":"crossref","unstructured":"Wang C, Wang J, Zhang X (2017) Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network. In: IEEE Int. Conf. on Acoustics, Speech and Signal Processing, New Orleans, USA, March 2017. IEEE, new Orleans, USA, pp 2437\u20132441","DOI":"10.1109\/ICASSP.2017.7952594"},{"key":"9490_CR29","doi-asserted-by":"crossref","unstructured":"Wang W, Lu X, Shen J, Crandall DJ, Shao L (2019) Zero-shot video object segmentation via attentive graph neural networks. In: international conference on computer vision, Seoul, Korea. pp. 9236\u20139245","DOI":"10.1109\/ICCV.2019.00933"},{"key":"9490_CR30","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.sigpro.2018.09.038","volume":"155","author":"Q Wang","year":"2019","unstructured":"Wang Q, Du P, Yang J, Wang G, Lei J, Hou C (2019) Transferred deep learning based waveform recognition for cognitive passive radar. Signal Process 155:259\u2013267. https:\/\/doi.org\/10.1016\/j.sigpro.2018.09.038","journal-title":"Signal Process"},{"issue":"6","key":"9490_CR31","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1049\/iet-rsn.2018.5549","volume":"13","author":"F Wang","year":"2019","unstructured":"Wang F, Yang C, Huang S, Wang H (2019) Automatic modulation classification based on joint feature map and convolutional neural network. IET Radar Sonar Navig 13(6):998\u20131003. https:\/\/doi.org\/10.1049\/iet-rsn.2018.5549","journal-title":"IET Radar Sonar Navig"},{"key":"9490_CR32","doi-asserted-by":"publisher","first-page":"107526","DOI":"10.1016\/j.sigpro.2020.107526","volume":"171","author":"Q Wang","year":"2020","unstructured":"Wang Q, Du P, Liu X, Yang J, Wang G (2020) Adversarial unsupervised domain adaptation for cross scenario waveform recognition. Signal Process 171:107526. https:\/\/doi.org\/10.1016\/j.sigpro.2020.107526","journal-title":"Signal Process"},{"issue":"6","key":"9490_CR33","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1049\/iet-rsn.2019.0436","volume":"14","author":"SJ Wei","year":"2020","unstructured":"Wei SJ, Qu QZ, Su H, Wang M, Shi J, Hao XJ (2020) Intra-pulse modulation radar signal recognition based on CLDN network. Iet Radar Sonar And Navigation 14(6):803\u2013810. https:\/\/doi.org\/10.1049\/iet-rsn.2019.0436","journal-title":"Iet Radar Sonar And Navigation"},{"key":"9490_CR34","doi-asserted-by":"publisher","first-page":"19733","DOI":"10.1109\/ACCESS.2017.2746140","volume":"5","author":"Z Wu","year":"2017","unstructured":"Wu Z, Zhou S, Yin Z, Ma B, Yang Z (2017) Robust automatic modulation classification under varying noise conditions. IEEE Access 5:19733\u201319741. https:\/\/doi.org\/10.1109\/ACCESS.2017.2746140","journal-title":"IEEE Access"},{"issue":"5","key":"9490_CR35","doi-asserted-by":"publisher","first-page":"75","DOI":"10.3390\/sym9050075","volume":"9","author":"M Zhang","year":"2017","unstructured":"Zhang M, Diao M, Gao L, Liu L (2017) Neural networks for radar waveform recognition. Symmetry-Basel 9(5):75. https:\/\/doi.org\/10.3390\/sym9050075","journal-title":"Symmetry-Basel"},{"key":"9490_CR36","doi-asserted-by":"publisher","first-page":"11074","DOI":"10.1109\/access.2017.2716191","volume":"5","author":"M Zhang","year":"2017","unstructured":"Zhang M, Diao M, Guo L (2017) Convolutional neural networks for automatic cognitive radio waveform recognition. IEEE Access 5:11074\u201311082. https:\/\/doi.org\/10.1109\/access.2017.2716191","journal-title":"IEEE Access"},{"issue":"2","key":"9490_CR37","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1049\/iet-rsn.2017.0265","volume":"12","author":"J Zhang","year":"2018","unstructured":"Zhang J, Li Y, Yin J (2018) Modulation classification method for frequency modulation signals based on the time-frequency distribution and CNN. IET Radar Sonar Navig 12(2):244\u2013249. https:\/\/doi.org\/10.1049\/iet-rsn.2017.0265","journal-title":"IET Radar Sonar Navig"},{"issue":"3","key":"9490_CR38","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1109\/tsipn.2019.2900201","volume":"5","author":"Z Zhang","year":"2019","unstructured":"Zhang Z, Wang C, Gan C, Sun S, Wang M (2019) Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD. IEEE Trans Signal Infor Process Net 5(3):469\u2013478. https:\/\/doi.org\/10.1109\/tsipn.2019.2900201","journal-title":"IEEE Trans Signal Infor Process Net"},{"issue":"99","key":"9490_CR39","doi-asserted-by":"publisher","first-page":"67366","DOI":"10.1109\/ACCESS.2020.2986330","volume":"8","author":"R Zhou","year":"2020","unstructured":"Zhou R, Liu F, Gravelle CW (2020) Deep learning for modulation recognition: a survey with a demonstration. IEEE Access 8(99):67366\u201367376. https:\/\/doi.org\/10.1109\/ACCESS.2020.2986330","journal-title":"IEEE Access"},{"key":"9490_CR40","doi-asserted-by":"publisher","first-page":"107393","DOI":"10.1016\/j.sigpro.2019.107393","volume":"169","author":"M Zhu","year":"2020","unstructured":"Zhu M, Li Y, Pan Z, Yang J (2020) Automatic modulation recognition of compound signals using a deep multi-label classifier: a case study with radar jamming signals. Signal Process 169:107393. https:\/\/doi.org\/10.1016\/j.sigpro.2019.107393","journal-title":"Signal Process"},{"issue":"10","key":"9490_CR41","doi-asserted-by":"publisher","first-page":"8255","DOI":"10.1109\/tgrs.2019.2919472","volume":"57","author":"S Zorzi","year":"2019","unstructured":"Zorzi S, Maset E, Fusiello A, Crosilla F (2019) Full-waveform airborne LiDAR data classification using convolutional neural networks. IEEE Trans Geosci Remote Sensing 57(10):8255\u20138261. https:\/\/doi.org\/10.1109\/tgrs.2019.2919472","journal-title":"IEEE Trans Geosci Remote Sensing"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09490-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09490-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09490-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T00:26:56Z","timestamp":1631233616000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09490-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,10]]},"references-count":41,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["9490"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09490-5","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,10]]},"assertion":[{"value":"1 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}