Comparison of Spectral Estimation Methods for Rapidly Varying Currents Obtained by High-Frequency Radar

Wang, Wei; Gill, Eric W.; Huang, Weimin

doi:10.1109/joe.2016.2591718

Cited by 4 publications

(1 citation statement)

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“…In particular, the use of Autoregressive (AR) modeling was applied by [16] within the framework of iceberg detection in HFR coastal measurements. Later on, [17] first proposed an AR approach in the context of oceanographic HFR, whose interest has been confirmed through subsequent studies [18,19,20]. To date, however, these methods have been confined to specific applications and have not yet earned their letters of nobility in the common HFR signal processing toolbox.…”

Section: Introductionmentioning

confidence: 99%

High-Frequency Radar Ocean Current Mapping at Rapid Scale with Autoregressive Modeling

Domps¹,

Dumas²,

Guérin³

et al. 2020

Preprint

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We use an Autoregressive (AR) approach combined with a Maximum Entropy Method (MEM) to estimate radial surface currents from coastal High-Frequency Radar (HFR) complex voltage time series. The performances of this combined AR-MEM model are investigated with synthetic HFR data and compared with the classical Doppler spectrum approach. It is shown that AR-MEM drastically improves the quality and the rate of success of the surface current estimation for short integration time. To confirm these numerical results, the same analysis is conducted with an experimental data set acquired with a 16.3 MHz HFR in Toulon. It is found that the AR-MEM technique is able to provide high-quality and high-coverage maps of surface currents even with very short integration time (about 1 minute) where the classical spectral approach can only fulfill the quality tests on a sparse coverage. Further useful application of the technique is found in the tracking of surface current at high-temporal resolution. Rapid variations of the surface current at the time scale of the minute are unveiled and shown consistent with a f −5/3 decay of turbulent spectra.

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