dc.contributor.author | Han, Peihua | |
dc.contributor.author | Li, Guoyuan | |
dc.contributor.author | Skjong, Stian | |
dc.contributor.author | Wu, Baiheng | |
dc.contributor.author | Zhang, Houxiang | |
dc.date.accessioned | 2021-11-04T11:42:08Z | |
dc.date.available | 2021-11-04T11:42:08Z | |
dc.date.created | 2021-10-21T10:46:21Z | |
dc.date.issued | 2021 | |
dc.identifier.isbn | 978-1-7281-9077-8 | |
dc.identifier.uri | https://hdl.handle.net/11250/2827857 | |
dc.description.abstract | Situation awareness is of great importance for autonomous ships. One key aspect is to estimate the sea state in a real-time manner. Considering the ship as a large wave buoy, the sea state can be estimated from motion responses without extra sensors installed. However, it is difficult to associate waves with ship motion through an explicit model since the hydrodynamic effect is hard to model. In this paper, a data-driven model is developed to estimate the sea state based on ship motion data. The ship motion response is analyzed through statistical, temporal, spectral, and wavelet analysis. Features from multi-domain are constructed and an ensemble machine learning model is established. Real-world data is collected from a research vessel operating on the west coast of Norway. Through the validation with the real-world data, the model shows promising performance in terms of significant wave height and peak period. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.ispartof | 2021 IEEE International Conference on Robotics and Automation (ICRA) | |
dc.title | Data-driven sea state estimation for vessels using multi-domain features from motion responses | en_US |
dc.type | Chapter | en_US |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.source.pagenumber | 2120-2126 | en_US |
dc.identifier.doi | 10.1109/ICRA48506.2021.9561261 | |
dc.identifier.cristin | 1947518 | |
dc.relation.project | Norges forskningsråd: 280703 | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |