{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:41:53Z","timestamp":1743050513798,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":14,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819964970"},{"type":"electronic","value":"9789819964987"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-6498-7_11","type":"book-chapter","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T20:41:21Z","timestamp":1697143281000},"page":"121-131","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sparse Adaptive Channel Estimation Based on\u00a0Multi-kernel Correntropy"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3601-1214","authenticated-orcid":false,"given":"Kun","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4620-6931","authenticated-orcid":false,"given":"Gang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9353-1086","authenticated-orcid":false,"given":"Mingzhu","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2128-2063","authenticated-orcid":false,"given":"Chen","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2486-6776","authenticated-orcid":false,"given":"Bei","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,13]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Cai, Y., Guo, H., Zhou, K., Xu, L.: Unmanned aerial vehicle cluster operations under the background of intelligentization. In: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM), pp. 525\u2013529. IEEE (2021)","DOI":"10.1109\/AIAM54119.2021.00110"},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"108048","DOI":"10.1016\/j.oceaneng.2020.108048","volume":"216","author":"YL Chen","year":"2020","unstructured":"Chen, Y.L., Ma, X.W., Bai, G.Q., Sha, Y., Liu, J.: Multi-autonomous underwater vehicle formation control and cluster search using a fusion control strategy at complex underwater environment. Ocean Eng. 216, 108048 (2020)","journal-title":"Ocean Eng."},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Beena, A., Pillai, S.S., Vijayakumar, N.: An improved adaptive sparse channel estimation method for next generation wireless broadband. In: 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 1\u20135. IEEE (2018)","DOI":"10.1109\/WiSPNET.2018.8538440"},{"key":"11_CR4","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1007\/s11277-015-2273-x","volume":"82","author":"H \u015eenol","year":"2015","unstructured":"\u015eenol, H.: Joint channel estimation and symbol detection for OFDM systems in rapidly time-varying sparse multipath channels. Wireless Pers. Commun. 82, 1161\u20131178 (2015)","journal-title":"Wireless Pers. Commun."},{"key":"11_CR5","unstructured":"Matz, G., Hlawatsch, F.: Time-varying communication channels: fundamentals, recent developments, and open problems. In: 2006 14th European Signal Processing Conference, pp. 1\u20135. IEEE (2006)"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Y., Gu, Y., Hero, A.O.: Sparse LMS for system identification. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3125\u20133128. IEEE (2009)","DOI":"10.1109\/ICASSP.2009.4960286"},{"issue":"1","key":"11_CR7","first-page":"213","volume":"66","author":"X Hong","year":"2016","unstructured":"Hong, X., Gao, J., Chen, S.: Zero-attracting recursive least squares algorithms. IEEE Trans. Veh. Technol. 66(1), 213\u2013221 (2016)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.automatica.2016.10.004","volume":"76","author":"B Chen","year":"2017","unstructured":"Chen, B., Liu, X., Zhao, H., Principe, J.C.: Maximum correntropy Kalman filter. Automatica 76, 70\u201377 (2017). https:\/\/doi.org\/10.1016\/j.automatica.2016.10.004","journal-title":"Automatica"},{"key":"11_CR9","doi-asserted-by":"publisher","unstructured":"Seth, S., Principe, J.C.: Compressed signal reconstruction using the correntropy induced metric. In: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3845\u20133848 (2008). https:\/\/doi.org\/10.1109\/ICASSP.2008.4518492","DOI":"10.1109\/ICASSP.2008.4518492"},{"issue":"7","key":"11_CR10","doi-asserted-by":"publisher","first-page":"2708","DOI":"10.1016\/j.jfranklin.2015.03.039","volume":"352","author":"W Ma","year":"2015","unstructured":"Ma, W., Qu, H., Gui, G., Xu, L., Zhao, J., Chen, B.: Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-gaussian environments. J. Franklin Inst. 352(7), 2708\u20132727 (2015)","journal-title":"J. Franklin Inst."},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Lu, M., Xing, L., Zheng, N., Chen, B.: Robust sparse channel estimation based on maximum mixture correntropy criterion. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207415"},{"key":"11_CR12","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.sigpro.2016.05.030","volume":"129","author":"X Zhang","year":"2016","unstructured":"Zhang, X., Li, K., Wu, Z., Fu, Y., Zhao, H., Chen, B.: Convex regularized recursive maximum correntropy algorithm. Signal Process. 129, 12\u201316 (2016)","journal-title":"Signal Process."},{"issue":"12","key":"11_CR13","doi-asserted-by":"publisher","first-page":"13500","DOI":"10.1109\/TCYB.2021.3110732","volume":"52","author":"B Chen","year":"2022","unstructured":"Chen, B., Xie, Y., Wang, X., Yuan, Z., Ren, P., Qin, J.: Multikernel correntropy for robust learning. IEEE Trans. Cybern. 52(12), 13500\u201313511 (2022). https:\/\/doi.org\/10.1109\/TCYB.2021.3110732","journal-title":"IEEE Trans. Cybern."},{"issue":"7","key":"11_CR14","doi-asserted-by":"publisher","first-page":"2588","DOI":"10.1109\/TSP.2005.849213","volume":"53","author":"B Weng","year":"2005","unstructured":"Weng, B., Barner, K.E.: Nonlinear system identification in impulsive environments. IEEE Trans. Signal Process. 53(7), 2588\u20132594 (2005)","journal-title":"IEEE Trans. Signal Process."}],"container-title":["Lecture Notes in Computer Science","Intelligent Robotics and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-6498-7_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T09:57:36Z","timestamp":1739008656000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-6498-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819964970","9789819964987"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-6498-7_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Robotics and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icira2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icira2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}