Joint Estimation of Vessel Parameter-Motion and Sea State
We consider the problem of real-time estimation of sea state and wave-induced motions on a moving vessel using onboard inertial sensors without knowing vessel's dynamic parameters (i.e., draught and breadth). This is crucial for vessel operational planning and performance, preventing structure...
| Main Authors: | , , , , , , |
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| Format: | Conference Paper |
| Published: |
2023
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| Online Access: | http://hdl.handle.net/20.500.11937/96499 |
| _version_ | 1848766159069904896 |
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| author | Nguyen, Hoa Van Luong, H. Sgarioto, D. Skvortsov, A. Arulampalam, S. Duffy, J. Ranasinghe, D.C. |
| author_facet | Nguyen, Hoa Van Luong, H. Sgarioto, D. Skvortsov, A. Arulampalam, S. Duffy, J. Ranasinghe, D.C. |
| author_sort | Nguyen, Hoa Van |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We consider the problem of real-time estimation of sea state and wave-induced motions on a moving vessel using onboard inertial sensors without knowing vessel's dynamic parameters (i.e., draught and breadth). This is crucial for vessel operational planning and performance, preventing structure failure, emissions reduction and fuel economy. This work proposes a new estimation approach by reformulating the conventional problem of sea state and vessel motion estimation (unknown input into a known dynamic system) as an input-state-parameter estimation problem of mass-spring-damper systems. We exploit the strong correlations between a vessel's vertical displacement and its rotation to develop a new estimation algorithm-Parameter-Sharing Extended-Augmented Kalman Filter (PS-EAKF)-for the problem to estimate the unidentified vessel parameters together with vessel motion (heave and pitch) and sea state. Experimental data from a scale-model vessel in regular head seas demonstrate the effectiveness and robustness of the proposed approach. |
| first_indexed | 2025-11-14T11:46:42Z |
| format | Conference Paper |
| id | curtin-20.500.11937-96499 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:46:42Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-964992025-01-09T06:28:28Z Joint Estimation of Vessel Parameter-Motion and Sea State Nguyen, Hoa Van Luong, H. Sgarioto, D. Skvortsov, A. Arulampalam, S. Duffy, J. Ranasinghe, D.C. We consider the problem of real-time estimation of sea state and wave-induced motions on a moving vessel using onboard inertial sensors without knowing vessel's dynamic parameters (i.e., draught and breadth). This is crucial for vessel operational planning and performance, preventing structure failure, emissions reduction and fuel economy. This work proposes a new estimation approach by reformulating the conventional problem of sea state and vessel motion estimation (unknown input into a known dynamic system) as an input-state-parameter estimation problem of mass-spring-damper systems. We exploit the strong correlations between a vessel's vertical displacement and its rotation to develop a new estimation algorithm-Parameter-Sharing Extended-Augmented Kalman Filter (PS-EAKF)-for the problem to estimate the unidentified vessel parameters together with vessel motion (heave and pitch) and sea state. Experimental data from a scale-model vessel in regular head seas demonstrate the effectiveness and robustness of the proposed approach. 2023 Conference Paper http://hdl.handle.net/20.500.11937/96499 10.23919/FUSION52260.2023.10224110 fulltext |
| spellingShingle | Nguyen, Hoa Van Luong, H. Sgarioto, D. Skvortsov, A. Arulampalam, S. Duffy, J. Ranasinghe, D.C. Joint Estimation of Vessel Parameter-Motion and Sea State |
| title | Joint Estimation of Vessel Parameter-Motion and Sea State |
| title_full | Joint Estimation of Vessel Parameter-Motion and Sea State |
| title_fullStr | Joint Estimation of Vessel Parameter-Motion and Sea State |
| title_full_unstemmed | Joint Estimation of Vessel Parameter-Motion and Sea State |
| title_short | Joint Estimation of Vessel Parameter-Motion and Sea State |
| title_sort | joint estimation of vessel parameter-motion and sea state |
| url | http://hdl.handle.net/20.500.11937/96499 |