{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T15:08:04Z","timestamp":1781622484299,"version":"3.54.5"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031189067","type":"print"},{"value":"9783031189074","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-18907-4_59","type":"book-chapter","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:03:53Z","timestamp":1666825433000},"page":"762-777","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Radar HRRP Target Recognition Method Based on\u00a0Conditional Wasserstein VAEGAN and\u00a01-D CNN"],"prefix":"10.1007","author":[{"given":"Jiaxing","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"key":"59_CR1","doi-asserted-by":"publisher","first-page":"29211","DOI":"10.1109\/ACCESS.2018.2842687","volume":"6","author":"K Liao","year":"2018","unstructured":"Liao, K., Si, J., Zhu, F., He, X.: Radar HRRP target recognition based on concatenated deep neural networks. IEEE Access 6, 29211\u201329218 (2018)","journal-title":"IEEE Access"},{"issue":"6","key":"59_CR2","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1049\/el.2015.3583","volume":"52","author":"J Liu","year":"2016","unstructured":"Liu, J., Fang, N., Wang, B.F., Xie, Y.J.: Scale-space theory-based multi-scale features for aircraft classification using HRRP. Electron. Lett. 52(6), 475\u2013477 (2016)","journal-title":"Electron. Lett."},{"key":"59_CR3","unstructured":"Lei, L., Wang, X., Xing, Y., Kai, B.: Multi-polarized HRRP classification by SVM and DS evidence theory. Control Decision 28(6) (2013)"},{"key":"59_CR4","unstructured":"Rui, L.I., Wang, X., Lei, L., Xue, A.: Ballistic target HRRP fusion recognition combining multi-class relevance vector machine and DS. Inf. Control (2017)"},{"key":"59_CR5","unstructured":"Ranzato, M., Boureau, Y.L., Lecun, Y.: Sparse feature learning for deep belief networks (2007)"},{"key":"59_CR6","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"EG Hinton","year":"2006","unstructured":"Hinton, E.G., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504\u2013507 (2006)","journal-title":"Science"},{"key":"59_CR7","unstructured":"Goodfellow, I.J., et al.: Generative Adversarial Networks, Jun 2014. arXiv:1406.2661 [cs, stat]. (Accessed 28 Sep 2020)"},{"key":"59_CR8","unstructured":"Yang, Y., Sun, J., Shengkang, Y.U., Peng, X.: High Resolution Range Profile Target Recognition Based on Convolutional Neural Network. Modern Radar (2017)"},{"key":"59_CR9","unstructured":"Guo, C., Jian, T., Congan, X. You, H., Sun, S.: Radar HRRP Target Recognition Based on Deep Multi-Scale 1D Convolutional Neural Network. J. Electron. Inf. Technol. 41(6), 1302\u20131309 (2019)"},{"issue":"1","key":"59_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13634-019-0603-y","volume":"2019","author":"J Wan","year":"2019","unstructured":"Wan, J., Chen, B., Xu, B., Liu, H., Jin, L.: Convolutional neural networks for radar HRRP target recognition and rejection. EURASIP J. Adv. Signal Process. 2019(1), 1\u201317 (2019). https:\/\/doi.org\/10.1186\/s13634-019-0603-y","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"59_CR11","unstructured":"Lu, W., Zhang, Y.S., Xu, C.: HRRP target recognition method based on bispectrum-spectrogram feature and deep convolutional neural network. Syst. Eng. Electron. 42(8) (2020)"},{"key":"59_CR12","unstructured":"Kingma, D., Welling, M.: Auto-Encoding Variational Bayes (2014)"},{"key":"59_CR13","unstructured":"Larsen, A., S\u00f8nderby, S., Winther, O.: Autoencoding beyond pixels using a learned similarity metric (2015)"},{"key":"59_CR14","unstructured":"Mirza, M., Osindero, S.: Conditional Generative Adversarial Nets (2014)"},{"key":"59_CR15","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN (2017)"},{"key":"59_CR16","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., et al.: Improved Training of Wasserstein GANs (2017)"},{"key":"59_CR17","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1002\/int.22302","volume":"36","author":"Q Xiang","year":"2020","unstructured":"Xiang, Q., Wang, X., Song, Y., Lei, L., Li, R., Lai, J.: One-dimensional convolutional neural networks for high-resolution range profile recognition via adaptively feature recalibrating and automatically channel pruning. Int. J. Intell. Syst. 36, 332\u2013361 (2020). https:\/\/doi.org\/10.1002\/int.22302","journal-title":"Int. J. Intell. Syst."},{"key":"59_CR18","doi-asserted-by":"publisher","unstructured":"Huang, S., Lei, K.: IGAN-IDS: an imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks. Ad Hoc Netw. 105, 10217 (2020). https:\/\/doi.org\/10.1016\/j.adhoc.2020.102177","DOI":"10.1016\/j.adhoc.2020.102177"},{"key":"59_CR19","doi-asserted-by":"crossref","unstructured":"Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. (JAIR) 16, 321\u2013357 (2002) https:\/\/doi.org\/10.1613\/jair.953","DOI":"10.1613\/jair.953"},{"key":"59_CR20","unstructured":"He, H., Bai, Y., Garcia, E., Li, S.: ADASYN: adaptive Synthetic Sampling Approach for Imbalanced Learning (2008)"},{"key":"59_CR21","unstructured":"Misra, D.: Mish: A Self Regularized Non-Monotonic Neural Activation Function (2019)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18907-4_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:15:13Z","timestamp":1666826113000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18907-4_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031189067","9783031189074"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18907-4_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/en.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"564","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"233","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.03","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.35","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}