A brute-force spectral approach for wave estimation using measured vessel motions

Nielsen, Ulrik Dam; Brodtkorb, Astrid H.; Sørensen, Asgeir J.

doi:10.1016/j.marstruc.2018.03.011

Cited by 46 publications

(25 citation statements)

References 23 publications

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“…This initial study is particularly relevant for DP (no forward speed), but in the meantime the procedure has been generalized to include advance speed and short-crested waves [30]Huskå endre denne.... For future work, a sensitivity study on the number of samples in the FFT of the vessel responses, and comparing the method when using the vessel RAOs instead of the closed-form expressions could be done.…”

Section: Resultsmentioning

confidence: 99%

Sea state estimation using vessel response in dynamic positioning

Brodtkorb

Nielsen

Sørensen

2018

Applied Ocean Research

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This paper presents a novel method for estimating the sea state parameters based on the heave, roll and pitch response of a vessel in dynamic positioning (DP), i.e., without forward speed. The algorithm finds the wave spectrum estimate from the response measurements by directly solving a set of linear equations, and as a result it is computationally efficient. The main vessel parameters are required as input. Apart from this the method is signal-based, with no assumptions on the wave spectrum shape. Performance of the proposed algorithm is demonstrated on full-scale experimental DP data of a vessel in three different sea states at head, bow, beam, quartering and following sea waves, respectively.

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Section: Resultsmentioning

confidence: 99%

Sea state estimation using vessel response in dynamic positioning

Brodtkorb

Nielsen

Sørensen

2018

Applied Ocean Research

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“…Another way of sea state estimation with the moored buoy analogy was presented by Nielsen (2018b) [36]. The described simple process relies on direct brute force estimation providing computational efficiency, very convenient to use in real time.…”

Section: Estimation Of Dwsmentioning

confidence: 99%

Improving the Performance of Dynamic Ship Positioning Systems: A Review of Filtering and Estimation Techniques

Lerga

Prpić-Oršić

et al. 2020

JMSE

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Various operations at sea, such as maintaining a constant ship position and direction, require a complex control system. Under such conditions, the ship needs an efficient positioning technique. Dynamic positioning (DP) systems provide such an application with a combination of the actuators mechanism, analyses of crucial ship variables, and environmental conditions. The natural forces of induced nonlinear waves acting on a ship’s hull interfere with the systems. To generate control signals for actuators accurately, sensor measurements should be filtered and processed. Furthermore, for safe and green routing, the forces and moments acting on the ship’s hull should be taken into account in terms of their prediction. Thus, the design of such systems takes into account the problem of obtaining data about the directional wave spectra (DWS). Sensor systems individually cannot provide high accuracy and reliability, so their measurements need to be combined and complemented. Techniques based on the recursive Kalman filter (KF) are used for this purpose. When some measurements are unavailable, the estimation procedure should predict them and, based on the comparison of theoretical and measured states, reduce the error variance of the analyzed signals. Different approaches for improving estimation algorithms have evolved over the years with the indication of improvement. This paper gives an overview of the state-of-the-art estimation and filtering techniques for providing optimum estimation states in DP systems.

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“…The remote sensing satellites is easily affected by the cloud, and the weather information just lags up to several hours, in general. Nowadays, many researchers have conducted extensive explorations on the identification of sea state based on the ship motion data directly [3], [4]. A *Corrosponding author Xu Cheng and Robert Skulstad have equal contribution.…”

Section: Introductionmentioning

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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A brute-force spectral approach for wave estimation using measured vessel motions

Cited by 46 publications

References 23 publications

Sea state estimation using vessel response in dynamic positioning

Sea state estimation using vessel response in dynamic positioning

Improving the Performance of Dynamic Ship Positioning Systems: A Review of Filtering and Estimation Techniques

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

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