Article(id=1311360868537958680, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.04.004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1768406400000, receivedDateStr=2026-01-15, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790582394965, onlineDateStr=2026-09-28, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790582394965, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790582394965, creator=13701087609, updateTime=1790582394965, updator=13701087609, issue=Issue{id=1311360859318870426, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='4', pageStart='1', pageEnd='185', issueExtLink='null', onlineDate='null', pubDate='1787587200000', pubDateStr='2026-08-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790582392768, creator='13701087609', updateTime=1790583829226, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311366884331716830, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311366884331716831, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1311360859318870426, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=30, endPage=38, ext={EN=ArticleExt(id=1311360868726702361, articleId=1311360868537958680, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Simulation and application of a ship track association algorithm based on dynamic time warping and dempster-shafer theory, columnId=1240655021340160960, journalTitle=Navigation of China, columnName=Intelligent Shipping, runingTitle=null, highlight=null, articleAbstract=

Reliable maritime situational awareness is a key requirement for the development of intelligent ships. Radar and the Automatic Identification System (AIS) are among the most widely deployed navigation sensors on board ships, and the association and fusion of their data provide the foundation for intelligent perception. However, ship state measurement data are often characterized by asynchronous sampling, missing observations, and uncertainty, which make track association challenging. To address this problem, this paper proposes a multi-target track association algorithm based on Dynamic Time Warping (DTW) and Dempster-Shafer Theory (DST). First, multi-source track data are temporally aligned and represented using the DTW method. Then, multidimensional track features are fused based on DST. A two-stage association decision strategy is further constructed to obtain the globally optimal matching of heterogeneous sensor data under constrained conditions. A multi-target track simulation dataset is established according to the observation characteristics of radar and AIS targets, and the applicability of the proposed algorithm is evaluated under different scenarios. The algorithm is further validated using sea-trial data collected from the intelligent research and training vessel Xin Hong Zhuan of Dalian Maritime University. The results show that the F₁-score of the proposed algorithm exceeds 0.9 in the track association task, while the performance fluctuation across different scenarios is only 9.03%. These results demonstrate that the proposed method has high accuracy and robustness, providing technical support for multi-source perception data fusion and maritime situational awareness in intelligent ships.

, authors=Congkai Song¹, Zhigao Zhao², Bing Han^{2, 3, *}, authorsList=Congkai Song, Zhigao Zhao, Bing Han, authorCompany=null, correspAuthors=Bing Han, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311360869074829594, articleId=1311360868537958680, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于动态时间规整与证据理论的航迹关联算法仿真与应用, columnId=1240655021528904650, journalTitle=中国航海, columnName=智能航运, runingTitle=null, highlight=null, articleAbstract=

可靠的海上航行环境态势感知是船舶智能化发展的关键，雷达和船舶自动识别系统（AIS）是船舶配备最多的导航设备，二者的数据关联与融合是实现智能感知的基础。针对船舶状态测量数据存在的异步、缺失及不确定等特点导致的航迹关联困难问题，提出一种基于动态时间规整（DTW）与证据理论（DST）的多目标航迹关联算法。通过动态时间规整方法对多源航迹数据进行时间序列对齐与特征表达；利用证据理论融合航迹多维特征信息；构建两阶段关联判别模型得到异构传感器数据在约束条件下的全局最优匹配。根据雷达与AIS的目标观测特性建模多目标航迹仿真数据集，评估轨迹关联算法在不同场景下的适用性。提出算法在大连海事大学智能研究与实训两用船“新红专”轮的试航阶段开展评估。结果表明：算法在航迹关联任务中的F₁-分数超过0.9，不同场景测试性能波动仅为9.03%，表现出较高的准确性与鲁棒性，为智能船舶多源感知信息融合与海上态势感知提供技术支撑。

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宋丛凯（2001—），男，博士生，研究方向为船舶智能化感知，（E-mail）kirito_sck@163.com

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宋丛凯（2001—），男，博士生，研究方向为船舶智能化感知，（E-mail）kirito_sck@163.com

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