dc.contributor.author | Han, Peihua | |
dc.contributor.author | Li, Guoyuan | |
dc.contributor.author | Cheng, Xu | |
dc.contributor.author | Skjong, Stian | |
dc.contributor.author | Zhang, Houxiang | |
dc.date.accessioned | 2021-10-21T06:38:19Z | |
dc.date.available | 2021-10-21T06:38:19Z | |
dc.date.created | 2021-04-19T08:51:23Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 1551-3203 | |
dc.identifier.uri | https://hdl.handle.net/11250/2824296 | |
dc.description.abstract | Situation awareness is essential 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. This task is challenging since the relationship between the wave and the ship motion is hard to model. Existing methods include a wave buoyanalogy (WBA) method, which assumes linearity between wave and ship motion, and a machine learning (ML) approach. Since the data collected from a vessel in the real world is typically limited to a small range of sea states, the ML method might suffer from catastrophic failure when the encountered sea state is not in the training dataset. This paper proposes a hybrid approach that combined the two methods above. The ML method is compensated by the WBA method based on the uncertainty of estimation results and, thus, the catastrophic failure can be avoided. Real-world historical data from the Research Vessel (RV) Gunnerus are applied to validate the approach. Results show that the hybrid approach improves estimation accuracy. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.title | An Uncertainty-aware Hybrid Approach for Sea State Estimation Using Ship Motion Responses | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | 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.journal | IEEE Transactions on Industrial Informatics | en_US |
dc.identifier.doi | 10.1109/TII.2021.3073462 | |
dc.identifier.cristin | 1904974 | |
dc.relation.project | Norges forskningsråd: 280703 | en_US |
dc.relation.project | Norges forskningsråd: 309323 | en_US |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
cristin.qualitycode | 2 | |