dc.contributor.author | Millefiori, Leonardo | |
dc.contributor.author | Braca, Paolo | |
dc.contributor.author | Arcieri, Gianfranco | |
dc.date.accessioned | 2019-06-19T08:35:21Z | |
dc.date.available | 2019-06-19T08:35:21Z | |
dc.date.issued | 2019/06 | |
dc.identifier.govdoc | CMRE-PR-2019-053 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.12489/793 | |
dc.description.abstract | Building on a novel methodology based on the Ornstein-Uhlenbeck (OU) process to perform accurate long-term predictions of future positions of ships at sea, we present a statistical approach to the detection of abrupt changes in the process parameter that represents the desired velocity of a ship. Proceeding from well-established change detection techniques, the proposed strategy is also computationally efficient and fit well with big data processing models and paradigms. We report results with a large real-world Automatic Identification System (AIS) data set collected by a network of terrestrial receivers in the Mediterranean Sea from June to August 2016. | en_US |
dc.format | 8 p. : ill. ; digital, PDF file | en_US |
dc.language.iso | en | en_US |
dc.publisher | CMRE | en_US |
dc.source | In: 2017 IEEE International Conference on Big Data (Big Data), 11-14 December 2017, Boston, MA, USA, pp. 1650-1657, doi: 10.1109/BigData.2017.8258101 | en_US |
dc.subject | Ship movements | en_US |
dc.subject | Ship tracking | en_US |
dc.subject | Maritime route prediction | en_US |
dc.subject | Trajectory estimation | en_US |
dc.subject | Automatic Identification Systems (AIS) | en_US |
dc.subject | Maritime surveillance | en_US |
dc.subject | Big data | en_US |
dc.subject | Ornstein-Uhlenbeck stochastic process | en_US |
dc.title | Scalable distributed change detection and its application to maritime traffic | en_US |
dc.type | Reprint (PR) | en_US |
dc.type | Papers and Articles | en_US |