Estimation of directional sea spectra from ship motions in sea trials

Pascoal, Ricardo; Perera, Lokukaluge P.; Soares, C. Guedes

doi:10.1016/j.oceaneng.2017.01.020

Cited by 58 publications

(41 citation statements)

References 23 publications

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“…Most of the research for sea state estimation recently is combining the wave-induced response measurements, which are collected by wave buoys or directly from the ship motion, and a mathematical model. 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.…”

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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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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“…References [7,8] presented the essential formulas, including the output matrix as Equation 11i.e., the use of vessel's hydrodynamics for wave spectrum estimation by utilizing the Kalman filter. Equations (1) and (2) are used as the system model equations.…”

Section: Main Formulas For Inverse Wave Estimationmentioning

confidence: 99%

“…As in Equation (10), this model has an important premise. It assumes that the measured vessel motion is the result of incoming waves that were weighted by a transfer function [7]. The vessel's transfer functions are complex, which means that they have amplitude and phase.…”

Section: Main Formulas For Inverse Wave Estimationmentioning

confidence: 99%

“…However, in the prediction of x, Equation 13was used instead of Equation 8i.e., the average value for three-time instants is applied as the state prediction value,x k+1 . In addition, P was computed by Equation 12instead of Equation (7). Equation 12was used for stability reasons and it is called the 'Joseph Form' [14].…”

Section: Main Formulas For Inverse Wave Estimationmentioning

confidence: 99%

“…Thus, in the case of large vessels, additional high-accuracy sensors may be utilized or other responses sensitive to high frequencies may be used. More research is needed on this issue and the Kalman filter setting alone may be the solution [7]. Figure 2 shows the actual sea conditions at North West Atlantic.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-Time Inverse Estimation of Ocean Wave Spectra from Vessel-Motion Sensors Using Adaptive Kalman Filter

2019

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The real-time inverse estimation of the ocean wave spectrum and elevation from a vessel-motion sensor is of significant practical importance, but it is still in the developing stage. The Kalman-filter method has the advantages of real-time estimation, cost reduction, and easy installation than other methods. Reasonable estimation of high-frequency waves is important in view of covering various sea states. However, if the vessel is less responsive for high-frequency waves, amplified noise may occur and cause overestimation problem there. In this paper, a configuration of Kalman filter with applying the principle of Wiener filter is proposed to suppress those over-estimations. Over-estimation is significantly reduced at high frequencies when the method is applied, and reliable real-time wave spectra and elevations can be obtained. The simulated sensor data was used, but the proposed algorithm has been proved to perform well for various sea states and different vessels. In addition, the proposed Kalman-filter technique is robust when it is applied to time-varying sea states.

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Measurement of Sea Waves

Piscopo

Rossi

Crenna

et al. 2022

Measurement for the Sea

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This chapter focuses on the analysis and measurement of sea waves. After a brief review about the generation and propagation of ocean waves, the most common wave spectra are briefly described. Subsequently, the spectral analysis of sea waves is discussed focusing on the most promising techniques that allow obtaining the sea state parameters starting from the wave elevation time history. Finally, two techniques useful to measure the sea wave parameters are outlined. The former is based on the reverse analysis of the motions a ship advancing at a constant speed in a seaway, and the latter focuses on the employment of coastal high-frequency radars.

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Estimation of directional sea spectra from ship motions in sea trials

Cited by 58 publications

References 23 publications

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

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

Real-Time Inverse Estimation of Ocean Wave Spectra from Vessel-Motion Sensors Using Adaptive Kalman Filter

Measurement of Sea Waves

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