{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:43:16Z","timestamp":1776811396248,"version":"3.51.2"},"reference-count":31,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2021,12,7]]},"abstract":"<jats:p>Classifying the motion pattern of marine targets is of important significance to promote target surveillance and management efficiency of marine area and to guarantee sea route safety. This paper proposes a moving target classification algorithm model based on channel extraction-segmentation-LCSCA-lp norm minimization. The algorithm firstly analyzes the entire distribution of channels in specific region, and defines the categories of potential ship motion patterns; on this basis, through secondary segmentation processing method, it obtains several line segment trajectories as training sample sets, to improve the accuracy of classification algorithm; then, it further uses the Leastsquares Cubic Spline Curves Approximation (LCSCA) technology to represent the training sample sets, and builds a motion pattern classification sample dictionary; finally, it uses lp norm minimized sparse representation classification model to realize the classification of motion patterns. The verification experiment based on real spatial-temporal trajectory dataset indicates that, this method can effectively realize the motion pattern classification of marine targets, and shows better time performance and classification accuracy than other representative classification methods.<\/jats:p>","DOI":"10.3233\/jcm-215383","type":"journal-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T13:42:42Z","timestamp":1628862162000},"page":"1695-1709","source":"Crossref","is-referenced-by-count":0,"title":["Classification method of marine target motion pattern based on spatial-temporal trajectories"],"prefix":"10.66113","volume":"21","author":[{"given":"Baichen","family":"Jiang","sequence":"first","affiliation":[{"name":"Coast Defense Army College, Naval Aeronautical University, Yantai, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Combat Service College, Naval Aeronautical University, Yantai, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Guan","sequence":"additional","affiliation":[{"name":"Combat Service College, Naval Aeronautical University, Yantai, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jialong","family":"Jin","sequence":"additional","affiliation":[{"name":"Coast Defense Army College, Naval Aeronautical University, Yantai, Shandong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"3","key":"10.3233\/JCM-215383_ref2","first-page":"46","article-title":"Empirical analysis of the influencing factors of China\u2019s maritime trade competitiveness","volume":"000","author":"Li","year":"2013","journal-title":"International Business Research"},{"issue":"3","key":"10.3233\/JCM-215383_ref3","first-page":"329","article-title":"Research progress on anomaly detection in vessel trajectory","volume":"31","author":"Zhou","year":"2017","journal-title":"Journal of Electronic and Instrumentation"},{"key":"10.3233\/JCM-215383_ref4","unstructured":"Z.K. 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