A new wave spectrum resembling procedure based on ship motion analysis

Piscopo, V.; Scamardella, A.; Gaglione, Salvatore

doi:10.1016/j.oceaneng.2020.107137

Cited by 23 publications

(17 citation statements)

References 23 publications

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“…where ∆ω is the circular frequency interval between two subsequent wave components, satisfying the inequality ∆ω ≤ 2π/T, to ensure the randomness of the wave signal over time interval T. Equation (6) was used in the current analysis with the main aim of resembling the original bimodal wave spectrum, obtained by the superposition of wind sea and swell components, and going back and forth from the frequency to time domains and vice versa. Particularly, as further detailed in Section 4.1, several random time histories were generated based on different combinations of wind sea and swell spectra and two signal lengths with durations of 3600 s (long) and 600 s (short), respectively.…”

Section: Input Wave Spectrum and Random Wave Generationmentioning

confidence: 99%

“…The assessment of wave spectra from the analysis of random wave elevations has been a widely investigated topic since the works of Mansard and Funke [ 1 , 2 ] and Battjes and val Vledder [ 3 ] because it is a key factor to detect sea state conditions and ensure the safety and navigation of ships [ 4 , 5 , 6 ]. Really, the assessment of wave spectrum parameters, namely significant wave height, wave peak period, and peak enhancement factor, has been revealed to be a quite-challenging issue since some aspects, such as the selection of a proper spectrum estimation technique, the minimum duration of the wave time signal, and the trade-off between spectral resolution and variance of the spectral estimator represent critical issues of the entire data processing procedure.…”

Section: Introductionmentioning

confidence: 99%

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Investigation on Spectrum Estimation Methods for Bimodal Sea State Conditions

Rossi

Crenna

Berardengo

et al. 2021

Sensors

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The reliable monitoring of sea state parameters is a key factor for weather forecasting, as well as for ensuring the safety and navigation of ships. In the current analysis, two spectrum estimation techniques, based on the Welch and Thomson methods, were applied to a set of random wave signals generated from a theoretical wave spectrum obtained by combining wind sea and swell components with the same prevailing direction but different combinations of significant wave heights, peak periods, and peak enhancement factors. A wide benchmark study was performed to systematically apply and compare the two spectrum estimation methods. In this respect, different combinations of wind sea spectra, corresponding to four grades of the Douglas Scale, were combined with three swell spectra corresponding to different swell categories. The main aim of the benchmark study was to systematically investigate the effectiveness of the Welch and Thomson methods in terms of spectrum restitution and the assessment of sea state parameters. The spectrum estimation methods were applied to random wave signals with different durations, namely 600 s (short) and 3600 s (long), to investigate how the record length affected the assembled sea state parameters, which, in turn, were assessed by the nonlinear least square method. Finally, based on the main outcomes of the benchmark study, some suggestions are provided to select the most suitable spectrum reconstruction method and increase the effectiveness of the assembled sea state parameters.

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Section: Input Wave Spectrum and Random Wave Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation on Spectrum Estimation Methods for Bimodal Sea State Conditions

Rossi

Crenna

Berardengo

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, it constitutes the input of the procedures for the estimation of the sea parameters which are then used for several purposes such as fatigue lifetime estimation [ 3 ], or to avoid ship dynamic instabilities [ 4 ]. Consequently, different approaches were proposed in the literature to estimate the sea features, starting from either onboard measurements (e.g., ship motion [ 5 , 6 , 7 , 8 , 9 ]) or time signals of wave level. Taking into consideration the latter approach, on the one hand, it is possible to successfully use non-parametric methods such as the Welch’s [ 10 ] and Thomson’s [ 11 ] methods for estimating sea spectrum [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Sea Spectral Estimation Using ARMA Models

Berardengo

Rossi

Crenna

2021

Sensors

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This paper deals with the spectral estimation of sea wave elevation time series by means of ARMA models. To start, the procedure to estimate the ARMA coefficients, based on the use of the Prony’s method applied to the auto-covariance series, is presented. Afterwards, an analysis on how the parameters involved in the ARMA reconstruction procedure—for example, the signal time length, the number of poles and data used—affect the spectral estimates is carried out, providing evidence on their effect on the accuracy of results. This allowed us to provide guidelines on how to set these parameters in order to make the ARMA model as accurate as possible. The paper focuses on mono-modal sea states. Nevertheless, examples also related to bi-modal sea states are discussed.

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“…Multi-GNSS single-frequency raw data collected by a smartphone are processed by the developed processing technique and different GNSSs combinations are considered, in order to verify the benefit of multi-GNSS integration in maritime context. The proposed algorithm can be suitable for maritime applications conducted on small boats not equipped with expensive sensors and it is especially performing in estimating the vertical component of the position, which is very useful for the analysis of the sea conditions (Montazeri et al, 2016;Piscopo et al, 2020). The positioning algorithm is tested on real data, collected by a smartphone Xiaomi Mi 8 located on board a moving ship; the receiver is a multi-GNSS, GPS, Glonass and BeiDou, device.…”

Section: Introductionmentioning

confidence: 99%

A Kalman Filter Single Point Positioning for Maritime Applications Using a Smartphone

Dardanelli¹,

Ferraioli²,

Martellato³

et al. 2020

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Different positioning techniques have been largely adopted for maritime applications that require high accuracy kinematic positioning. The main objective of the paper is the performance assessment of a Single Point Positioning algorithm (SPP), with a Kalman filter (KF) estimator, adapted for maritime applications. The KF has been chosen as estimation technique due to the ability to consider both the state vector dynamic and the measurements. Particularly, in order to compute an accurate vertical component of the position, suitable for maritime applications, the KF settings have been modified by tuning the covariance matrix of the process noise. The algorithm is developed in Matlab environment and tested using multi-GNSS single-frequency raw data, collected by a smartphone located on board a moving ship. The algorithm performance evaluation is carried out in position domain and the results show an enhancement of meter order on vertical component compared to the classical SPP based on Least Square estimation technique. In addition, different GNSSs configurations are considered to verify the benefits of their integration in terms of accuracy, solution availability and geometry.

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A new wave spectrum resembling procedure based on ship motion analysis

Cited by 23 publications

References 23 publications

Investigation on Spectrum Estimation Methods for Bimodal Sea State Conditions

Investigation on Spectrum Estimation Methods for Bimodal Sea State Conditions

Sea Spectral Estimation Using ARMA Models

A Kalman Filter Single Point Positioning for Maritime Applications Using a Smartphone

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