In this article, 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 16.15-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 min) 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 an $\displaystyle f^{-5/3}$ decay of turbulent spectra.

中文翻译：

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使用自回归建模快速绘制高频雷达洋流图

在本文中，我们使用自回归 (AR) 方法结合最大熵方法 (MEM) 来估计沿海高频雷达 (HFR) 复杂电压时间序列的径向表面电流。使用合成的 HFR 数据研究了这种组合 AR-MEM 模型的性能，并与经典的多普勒频谱方法进行了比较。结果表明，AR-MEM 在短积分时间内显着提高了表面电流估计的质量和成功率。为了确认这些数值结果，我们对土伦的 16.15-MHz HFR 采集的实验数据集进行了相同的分析。发现 AR-MEM 技术能够提供高质量和高覆盖率的表面电流图，即使积分时间非常短（约 1 分钟），而经典的光谱方法只能满足稀疏覆盖的质量测试. 在高时间分辨率下跟踪表面电流中发现了该技术的进一步有用应用。在分钟的时间尺度上表面电流的快速变化被揭示并显示出与$\displaystyle f^{-5/3}$ 湍流光谱的衰减。