Kalman filtering of vessel motions for ocean wave directional spectrum estimation

Pascoal, Ricardo; Soares, C. Guedes

doi:10.1016/j.oceaneng.2009.01.013

Cited by 67 publications

(55 citation statements)

References 11 publications

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“…Although inherent problems of the wave buoy analogy with regard to filtering have been reported (e.g. Simos et al, [49]; Nielsen, [37]; Pascoal and Guedes Soares, [44]), the wave buoy analogy is, in theory, capable of estimating exactly those waves which are of importance to the ship, in the operational and navigational sense. That is, the wave buoy analogy estimates the waves to which the ship responds (Nielsen,[39], [40]).…”

Section: Introductionmentioning

confidence: 99%

A novel approach for navigational guidance of ships using onboard monitoring systems

Nielsen

Jensen

2011

Ocean Engineering

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Section: Introductionmentioning

confidence: 99%

A novel approach for navigational guidance of ships using onboard monitoring systems

Nielsen

Jensen

2011

Ocean Engineering

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“…The previous work focused on the field of frequency domain analysis where the wave (energy) spectrum would be given [7], [8], [9], [10], [11], [2]. Pascoal and Soares [12] proposed a Kalman filtering based method which relies on the accurate RAOs in time domain only. Nielsen et al [13] also calculated the sea state directly in time domain on the basis of the measured response and corresponding theory.…”

Section: A Sea State Estimationmentioning

confidence: 99%

Modeling and Analysis of Motion Data from Dynamically Positioned Vessels for Sea State Estimation

Cheng

Skulstad

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Developing a reliable model to identify the sea state is significant for the autonomous ship. This paper introduces a novel deep neural network model (SeaStateNet) to estimate the sea state based on the ship motion data from dynamically positioned vessels. The SeaStateNet mainly consists of three components: an Long-Short-Term Memory (LSTM) recurrent neural network to capture the long dependency in the ship motion data; a convolutional neural network (CNN) to extract time-invariant features; and a Fast Fourier Transform (FFT) block to extract frequency features. A feature fusion layer is designed to learn the degree affected by each component. The proposed model is applied directly to the raw time series data, without needing of any hand-engineered features. A sensitivity analysis (SA) method is applied to assess the influence of data preprocessing. Through benchmark test and experiment on ship motion dataset, SeaStateNet is verified effective for sea state estimation. The investigation on real-time test further shows the practicality of the proposed model.

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“…JONSWAP spectrum here) perfectly represents the actual wave spectrum, is also sceptical. This has lead to wave estimation studies based on nonparametric methods [9][10][11].…”

Section: Uncertainty Sourcesmentioning

confidence: 99%

Uncertainties in ship-based estimation of waves and responses

Montazeri

Jensen

Nielsen

2015

OCEANS 2015 - MTS/IEEE Washington

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Abstract-Real time estimation of waves and ship responses using onboard measurements has been under investigation in recent years. This has been done using different methods, including parametric and non-parametric models. Since none of the methods are believed to be fully accurate, it is important to assign an uncertainty measure to the waves and responses that are being estimated. In this paper, a parametric model approach based on moments of responses is considered for wave estimation. A method based on linear error propagation is introduced to assess the uncertainty of wave estimations. The uncertainty of response calculation based on the estimated wave is also quantified.

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Kalman filtering of vessel motions for ocean wave directional spectrum estimation

Cited by 67 publications

References 11 publications

A novel approach for navigational guidance of ships using onboard monitoring systems

A novel approach for navigational guidance of ships using onboard monitoring systems

Modeling and Analysis of Motion Data from Dynamically Positioned Vessels for Sea State Estimation

Uncertainties in ship-based estimation of waves and responses

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