Application of conventional marine radars for measuring ocean wave fields in shallow water conditions

Borge, José Carlos Nieto; Mata-Moya, David; Jarabo-Amores, P.; Reichert, Konstanze; Hessner, Katrin

doi:10.1109/igarss.2010.5652640

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…From Equation ( 9), it is clear to find that the group velocity indicates the consistent time zone and distances zone. Moreover, for irregular cases, the linear wave components can be obtained by FFT and each wave component can be calculated by using Equation (9). Equation ( 9) is also the basis for building the discrete error function in Section 3.2.…”

Section: The Propagation Velocity In Dswpmentioning

confidence: 99%

“…Measuring the wave elevation is the preparation for wave prediction. Many measurement technologies have been developed to help measure the wave elevation or wave fields, such as X-band radar [9], and LIDAR [10][11][12]. Another direct measurement device is the wave buoy, which measures the wave elevation by the reaction between the waves and mooring system [13,14].…”

Section: Introductionmentioning

confidence: 99%

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The Propagation Velocity and Influences of Environmental Factors of Deterministic Sea Wave Prediction in the Long Crest Wave

Wang,

Chen,

et al. 2024

JMSE

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Ocean waves are one of the leading environmental factors that cause motion of the ocean’s structure. Wave prediction is of great significance for the safety of marine structures. The deterministic sea wave prediction (DSWP) has been focused on because it provided an accurate temporal wave surface. The propagation velocity of wave components is one of the critical problems in DSWP. In this paper, the research of propagation velocity is focused on. The Taylor expansion to wave number is used to prove that the group velocity is the propagation velocity of wave components. The simulated irregular long crest wave data is generated. Utilizing the simulated data, the calculated wave surfaces based on group velocity are consistent with the simulated results. Meanwhile, the comparisons of calculated results based on the group velocity and phase velocity are given. Then, a tank experiment is set to verify the prediction results. To further investigate the prediction performance under different conditions, the influences of environmental factors, including the wind speed, water depth and sea state are analyzed in this paper.

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Section: The Propagation Velocity In Dswpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Propagation Velocity and Influences of Environmental Factors of Deterministic Sea Wave Prediction in the Long Crest Wave

Wang,

Chen,

et al. 2024

JMSE

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“…The classical approach includes Fourier analysis and a linear dispersion relationship to recognize wave signals within the temporal series of radar data. The method necessitates the incorporation of modulation transfer functions and calibration coefficients specific to individual radar antennas [2], thereby constraining its generalizability. Despite this limitation, the classical methodology remains extensively applied for real-time estimation of ocean wave parameters, processing the back-scatter spectrum derived from radar images [3].…”

Section: Improving Data-driven Estimation Of Significant Wave Height ...mentioning

confidence: 99%

“…The covolutional core weights are frozen. After 20 epochs, we start stage 2 2 In investigations concerning the application of statistical models, particularly ANNs, for the analysis of remote sensing data, it is crucial to account for the autocorrelation inherent in the observational time series dataset. Owing to the inherent evolution of underlying physical phenomena, consecutive observations may demonstrate substantial autocorrelation, thereby influencing the accuracy of the model [16].…”

Section: Training and Evaluationmentioning

confidence: 99%

Improving Data-Driven Estimation of Significant Wave Height through Preliminary Training on Synthetic X-band Radar Sea Clutter Imagery

Rezvov,

Krinitskiy,

Gavrikov

et al. 2023

Preprint

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X-band marine radar captures signal reflected from the sea surface. Theoretical studies indicate that the initial unfiltered signal contains meaningful information including parameters related to wind waves. Traditional methods for estimating significant wave height (SWH) rely on the physical laws describing signal reflection from rough surfaces. However, recent studies suggest the feasibility of employing artificial neural networks (ANNs) for SWH approximation. Both classical and ANN-based approaches necessitate costly in situ data. In this study, we propose generating synthetic radar images with specified wind wave parameters using Fourier-based approach and Pierson–Moskowitz wave spectrum as a viable alternative. We generate synthetic images and use them for unsupervised learning approach to train a convolutional component of an ANN. After that, we train a regression ANN based on the previous convolutional component to obtain SWH back from synthetic images. Then, we apply preliminary trained weights for the same regression model to train SWH approximation on the dataset of real sea clutter images. In this study, we demonstrate the increase in the accuracy of SWH estimation from radar images with the preliminary training on synthetic data.

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“…Sensitivity studies are carried out based on simulated data, defining the operational limits of nautical X-Band radar within that context [25]. Nautical X-band radar data is seen as complementary to satellite data.…”

Section: Ground Based Monitoring Technology (Gbmt)mentioning

confidence: 99%

A Marine Information System for Environmental Monitoring: ARGO-MIS

Pieri

Cocco²,

Salvetti

2018

JMSE

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Sea shipping routes have become very crowded and this, coupled with an always increasing demand of oil based products, contributes to the increase in maritime traffic density, as a consequence pollution risks have increased. Therefore, it is important to have information systems capable of detecting and monitoring environmental endangering situations like oil spills at sea. In this paper, a Marine Information System, acting as an integrated and inter-operable monitoring tool is proposed and discussed. The discussion focuses on a system that is able to integrate different data acquired from various electronic sensors, and that is inter-operable among marine operators and ship traffic authorities. The available data on the system are all geo-referenced, and flows seamlessly through the system, where they are integrated in a consistent and usable manner. An important result of this integration is the capability to produce a collection of proactive services such as Decision Support ones, which can be used to improve the functionalities and facilities concerned in an intervention operation. Through the implementation of these services, we aim to demonstrate how an efficient environmental management system could benefit from being supported by a Marine Information System that can provide the dynamic links between different data, models and actors.

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Application of conventional marine radars for measuring ocean wave fields in shallow water conditions

Cited by 6 publications

References 7 publications

The Propagation Velocity and Influences of Environmental Factors of Deterministic Sea Wave Prediction in the Long Crest Wave

The Propagation Velocity and Influences of Environmental Factors of Deterministic Sea Wave Prediction in the Long Crest Wave

Improving Data-Driven Estimation of Significant Wave Height through Preliminary Training on Synthetic X-band Radar Sea Clutter Imagery

A Marine Information System for Environmental Monitoring: ARGO-MIS

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