Extraction of swell parameters from simulated noisy HF radar signals

Shen, Chengxi; Gill, Eric W.; Huang, Weimin

doi:10.1109/radar.2013.6585983

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…The spreading spectra with the Gaussian noise can be derived by a periodogram method [14,15]. The simulated spectra from 150 s time series are shown in Figure 15, where f 0 ¼ 5:283 MHz, ρ ¼ 100 km, v ¼ 5:07 m=s, Δρ ¼ 5 km, and α * slowly increasing from 0 ∘ in the stern to 45 ∘ at the prow of shipborne platform.…”

Section: Effect Of External Gaussian Noisementioning

confidence: 99%

Remote Sensing with Shipborne High-Frequency Surface-Wave Radar

Xie¹,

Sun²,

Ji³

et al. 2018

Recent Advances and Applications in Remote Sensing

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High-frequency surface-wave radar (HFSWR) has been successfully applied for moving target detection and remote sensing of ocean surface dynamic parameters for decades. Compared with conventional instruments such as buoys, anemometers, and microwave radars, HFSWR can be employed to an all-weather and all-time surveillance far beyond the visible horizon. Moreover, based on agility and maneuverability, shipborne HFSWR can not only enhance the survivability in complex ocean environment but also enlarge the detection distance on open sea, which will gradually become a popular deployment situation. In this chapter, ocean surface cross sections for shipborne HFSWR with linear platform motion and sway motion are derived theoretically. Then, the methods for ocean surface wind direction, wind field, and current extraction are presented. The computer simulations and experimental results of the real data are given to verify the detection accuracy and the distance limit of the abovementioned methods.

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Section: Effect Of External Gaussian Noisementioning

confidence: 99%

Remote Sensing with Shipborne High-Frequency Surface-Wave Radar

Xie¹,

Sun²,

Ji³

et al. 2018

Recent Advances and Applications in Remote Sensing

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“…High-frequency radar (HFR) offers another way to monitor ocean swell [11][12][13][14]. These HFRs are land-based ground-wave radars.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Swell Height Using Spaceborne GNSS-R Data from Eight CYGNSS Satellites

Park

et al. 2022

Remote Sensing

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Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) technology has opened a new window for ocean remote sensing because of its unique advantages, including short revisit period, low observation cost, and high spatial-temporal resolution. In this article, we investigated the potential of estimating swell height from delay-Doppler maps (DDMs) data generated by spaceborne GNSS-R. Three observables extracted from the DDM are introduced for swell height estimation, including delay-Doppler map average (DDMA), the leading edge slope (LES) of the integrated delay waveform (IDW), and trailing edge slope (TES) of the IDW. We propose one modeling scheme for each observable. To improve the swell height estimation performance of a single observable-based method, we present a data fusion approach based on particle swarm optimization (PSO). Furthermore, a simulated annealing aided PSO (SA-PSO) algorithm is proposed to handle the problem of local optimal solution for the PSO algorithm. Extensive testing has been performed and the results show that the swell height estimated by the proposed methods is highly consistent with reference data, i.e., the ERA5 swell height. The correlation coefficient (CC) is 0.86 and the root mean square error (RMSE) is 0.56 m. Particularly, the SA-PSO method achieved the best performance, with RMSE, CC, and mean absolute percentage error (MAPE) being 0.39 m, 0.92, and 18.98%, respectively. Compared with the DDMA, LES, TES, and PSO methods, the RMSE of the SA-PSO method is improved by 23.53%, 26.42%, 30.36%, and 7.14%, respectively.

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“…Methods have been developed to produce swell and wind-wave results from phased array radars; these methods are described in [5][6][7][8][9][10], along with a comparison to model results and buoy measurements. We also note that bimodal ocean wave monitoring from spaceborne remote sensing devices provides the powerful monitoring of swells propagating across the ocean [11,12].…”

Section: Introductionmentioning

confidence: 99%

Progress in Compact HF Radar Measurement of Bimodal Ocean Wave Parameters

Lipa

Daugharty²,

Losekoot

et al. 2022

Remote Sensing

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We describe an extension of methods used for the derivation of wave information from compact high frequency radar systems and give examples of observations which are compared to measurements from wave buoys close to the radar coverage area. Previous methods were based on the application of the Pierson–Moskowitz wave energy model in the analysis of radar spectra. We describe the extension of the methods to apply a bimodal ocean wave spectral model which includes two different spectra covering different wave frequencies, e.g., from swell and wind-waves. Results are presented from data sets obtained from radar stations located in the USA and Portugal.

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Extraction of swell parameters from simulated noisy HF radar signals

Cited by 7 publications

References 8 publications

Remote Sensing with Shipborne High-Frequency Surface-Wave Radar

Remote Sensing with Shipborne High-Frequency Surface-Wave Radar

Estimation of Swell Height Using Spaceborne GNSS-R Data from Eight CYGNSS Satellites

Progress in Compact HF Radar Measurement of Bimodal Ocean Wave Parameters

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