Signal-to-noise ratio analysis to estimate ocean wave heights from X-band marine radar image time series

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

doi:10.1049/iet-rsn:20070027

Cited by 130 publications

(97 citation statements)

References 12 publications

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“…Therefore, wave height cannot be directly determined from the radar-derived wave spectra. For estimating wave height, a widely accepted approach is based on a technique originally developed for SAR applications [60], in which H S is assumed to be proportional to the square root of the signal-to-noise ratio (SNR) [61]. The signal is assumed as the total energy of the estimated wave spectrum and the noise is computed as the energy due to the speckle caused by the sea surface roughness.…”

Section: A Traditional Algorithmmentioning

confidence: 99%

“…Then, the SNR may be computed from the wave and BGN spectra [61]. Finally, a linear model relating H S to the square root of SNR can be constructed as…”

Section: A Traditional Algorithmmentioning

confidence: 99%

“…After the linear model is obtained, H S can be derived from the SNR for each radar image sequence. The SNR-based method has been tested by many researchers with various datasets [2,10,19,20,[61][62][63]. These test results showed that the correlation coefficients between H S measured by buoy and radar were 0.71∼0.89, and the RMS differences were 0.18∼0.42 m. In [63], it was found that the difference of buoy and radar positions mainly accounted for the low correlation of 0.71 due to a significant change of bathymetry.…”

Section: A Traditional Algorithmmentioning

confidence: 99%

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Ocean Wind and Wave Measurements Using X-Band Marine Radar: A Comprehensive Review

2017

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Ocean wind and wave parameters can be measured by in-situ sensors such as anemometers and buoys. Since the 1980s, X-band marine radar has evolved as one of the remote sensing instruments for such purposes since its sea surface images contain considerable wind and wave information. The maturity and accuracy of X-band marine radar wind and wave measurements have already enabled relevant commercial products to be used in real-world applications. The goal of this paper is to provide a comprehensive review of the state of the art algorithms for ocean wind and wave information extraction from X-band marine radar data. Wind measurements are mainly based on the dependence of radar image intensities on wind direction and speed. Wave parameters can be obtained from radar-derived wave spectra or radar image textures for non-coherent radar and from surface radial velocity for coherent radar. In this review, the principles of the methodologies are described, the performances are compared, and the pros and cons are discussed. Specifically, recent developments for wind and wave measurements are highlighted. These include the mitigation of rain effects on wind measurements and wave height estimation without external calibrations. Finally, remaining challenges and future trends are discussed.

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Section: A Traditional Algorithmmentioning

confidence: 99%

“…Then, the SNR may be computed from the wave and BGN spectra [61]. Finally, a linear model relating H S to the square root of SNR can be constructed as…”

Section: A Traditional Algorithmmentioning

confidence: 99%

Section: A Traditional Algorithmmentioning

confidence: 99%

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Ocean Wind and Wave Measurements Using X-Band Marine Radar: A Comprehensive Review

2017

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“…Unfortunately, the significant wave height H s cannot be estimated directly using the Cartesian Fourier transformation method since the wave spectrum does not reflect the actual ocean wave energy and a calibration process is needed [25]. A recent study proposed a new method using the shadowing in radar images to estimate the significant wave height without the need for calibration [26].…”

Section: Review Of Wave Spectrum Estimation Using Marine Radarmentioning

confidence: 99%

An Adaptive Method of Wave Spectrum Estimation Using X-Band Nautical Radar

et al. 2015

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Abstract:In this paper, the dependency of wave spectrum estimation on the analysis window orientation for X-band marine radar data is investigated. The investigation is made for the case where both wind wave and swell components are present. Our results show that more accurate and reliable wave spectrum estimates are obtained when one analysis window for each component (wind waves or swell) is used and is oriented in the up-wave direction of that component. The final wave spectrum estimation is found by averaging the output of those analysis windows. Since the direction of wind waves and swell is not known a priori, a new method is proposed to recursively determine the number and orientation of analysis windows. This method is referred to as the Adaptive Recursive Positioning Method (ARPM). For validation, ocean wave spectra are estimated from X-band marine radar field data using the ARPM and the standard method, using uniformly distributed analysis windows. The results from both methods are compared to ground truth wave spectra acquired using waverider buoy data. Results have shown that the ARPM produces a 10% improvement in the agreement (represented by the correlation coefficient) between the ground truth wave spectrum and the marine radar estimated wave spectrum. This improvement is reflected in a 15% to 30% accuracy enhancement in the wave period estimation and 6 • in peak wave direction. The ARPM not only increases the accuracy of wave period and direction estimation, but it also increases the method's reliability by producing a lower error standard deviation. Although these improvements are at the price of extra computational time, it has been found that this overhead is acceptable since it is far from the upper bound that requires offline analysis.

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“…Using these data sets, the spatial and temporal variability of the sea surface is analyzed to extract an estimation of the so-called wave spectrum [4,7,9]. From this wave spectrum, typical sea state parameters, such us characteristic wave periods, wavelengths and wave propagation directions, can be derived to describe each sea state [6].…”

Section: Introductionmentioning

confidence: 99%

Estimate of significant wave height from non-coherent marine radar images by multilayer perceptrons

Vicen-Bueno

Lido-Muela

Borge

2012

EURASIP J. Adv. Signal Process.

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One of the most relevant parameters to characterize the severity of ocean waves is the significant wave height (H s ). The estimate of H s from remotely sensed data acquired by non-coherent X-band marine radars is a problem not completely solved nowadays. A method commonly used in the literature (standard method) uses the square root of the signal-to-noise ratio (SNR) to linearly estimate H s . This method has been widely used during the last decade, but it presents some limitations, especially when swell-dominated sea states are present. To overcome these limitations, a new non-linear method incorporating additional sea state information is proposed in this article. This method is based on artificial neural networks (ANNs), specifically on multilayer perceptrons (MLPs). The information incorporated in the proposed MLP-based method is given by the wave monitoring system (WaMoS II) and concerns not only to the square root of the SNR, as in the standard method, but also to the peak wave length and mean wave period. Results for two different platforms (Ekofisk and FINO 1) placed in different locations of the North Sea are presented to analyze whether the proposed method works regardless of the sea states observed in each location or not. The obtained results empirically demonstrate how the proposed non-linear solution outperforms the standard method regardless of the environmental conditions (platform), maintaining real-time properties.

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Signal-to-noise ratio analysis to estimate ocean wave heights from X-band marine radar image time series

Cited by 130 publications

References 12 publications

Ocean Wind and Wave Measurements Using X-Band Marine Radar: A Comprehensive Review

Ocean Wind and Wave Measurements Using X-Band Marine Radar: A Comprehensive Review

An Adaptive Method of Wave Spectrum Estimation Using X-Band Nautical Radar

Estimate of significant wave height from non-coherent marine radar images by multilayer perceptrons

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