{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T21:49:26Z","timestamp":1781992166135,"version":"3.54.5"},"reference-count":35,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62031014"],"award-info":[{"award-number":["62031014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In high-traffic port areas, vessel traffic-management systems (VTMS) are essential for managing ship movements and preventing collisions. However, inaccuracies and omissions in the Automatic Identification System (AIS), along with frequent false tracks generated by radar false alarms in complex environments, can compromise VTMS stability. To address the challenges of establishing consistent navigation and improving trajectory quality, this study introduces a novel method to directly identify AIS-matched trajectories from radar plots. This approach treats radar points as probability clouds, generating a multi-dimensional information layer by stacking these clouds after differential transformations based on AIS data. The resulting layer undergoes filtering and clustering to extract point sets that align with AIS data, effectively isolating matching trajectories. The algorithm, validated with simulated data, rapidly identifies target trajectories amid extensive interference without requiring strict parameter adjustments. In measured data, the algorithm rapidly provides matching trajectories, although further human judgment is still required due to the potential absence of true values in measured data.<\/jats:p>","DOI":"10.3390\/rs16234360","type":"journal-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T04:21:36Z","timestamp":1732249296000},"page":"4360","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Fast Algorithm for Matching AIS Trajectories with Radar Point Data in Complex Environments"],"prefix":"10.3390","volume":"16","author":[{"given":"Jialuo","family":"Xu","sequence":"first","affiliation":[{"name":"School of Electronic Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Suo","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqing","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6570-1516","authenticated-orcid":false,"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115373","DOI":"10.1016\/j.oceaneng.2023.115373","article-title":"Intelligent marine area supervision based on AIS and radar fusion","volume":"285","author":"Wang","year":"2023","journal-title":"Ocean. 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