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Joint Estimation of Vessel Parameter-Motion and Sea State

EasyChair Preprint no. 10428

7 pagesDate: June 21, 2023


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.

Keyphrases: condition monitoring, Condition monitoring., Input-state-parameter estimation problems, Mass Spring Damper System, sea waves

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Hoa Van Nguyen and Hao Luong and Daniel Sgarioto and Alex Skvortsov and Sanjeev Arulampalam and Jonathan Duffy and Damith C. Ranasinghe},
  title = {Joint Estimation of Vessel Parameter-Motion and Sea State},
  howpublished = {EasyChair Preprint no. 10428},

  year = {EasyChair, 2023}}
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