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    Joint Estimation of Vessel Parameter-Motion and Sea State

    96263.pdf (1.232Mb)
    Access Status
    Open access
    Authors
    Nguyen, Hoa Van
    Luong, H.
    Sgarioto, D.
    Skvortsov, A.
    Arulampalam, S.
    Duffy, J.
    Ranasinghe, D.C.
    Date
    2023
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Nguyen, H.V. and Luong, H. and Sgarioto, D. and Skvortsov, A. and Arulampalam, S. and Duffy, J. and Ranasinghe, D.C. 2023. Joint Estimation of Vessel Parameter-Motion and Sea State. In Proceedings of 2023 26th International Conference on Information Fusion (FUSION), 27-30 June 2023, Charleston, SC, USA.
    Source Title
    2023 26th International Conference on Information Fusion, FUSION 2023
    Source Conference
    2023 26th International Conference on Information Fusion (FUSION)
    DOI
    10.23919/FUSION52260.2023.10224110
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/96499
    Collection
    • Curtin Research Publications
    Abstract

    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.

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