Nonlinear Ocean Wave Reconstruction Algorithms Based on Simulated Spatiotemporal Data Acquired by a Flash LIDAR Camera
Nonlinear Ocean Wave Reconstruction Algorithms Based on Simulated Spatiotemporal Data Acquired by a Flash LIDAR Camera
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基于闪光激光雷达相机采集的模拟时空数据的非线性海浪重建算法
DOI：
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发表时间：
2014
影响因子：
8.2
通讯作者：
C. Guérin
中科院分区：
文献类型：
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作者：
F. Nouguier;S. Grilli;C. Guérin
我们报告了基于高频闪光检测和测距相机进行的空间观测的自由表面重建算法的开发，以预测海浪。我们假设相机安装在船上，处于向前看的位置，并指向其路径前方的一定距离，从而产生时空波高程数据的样本。由于几何形状的原因，测量点的密度随着距相机的距离逐渐减小（即变得稀疏）。自由表面重建算法首先是针对线性一维和二维不规则表面模型开发和验证的，其振幅系数是在最小化模拟表面高程与测量值的均方误差的基础上估计的，在空间和时间上（对于指定的时间初始化周期）。在此报告的验证测试中，不规则的海洋表面是根据定向 Pierson-Moskowitz 或 Elfouhaily 光谱生成的，并通过将激光射线与每个生成的表面进行几何相交来构建模拟 LIDAR 数据集。一旦根据（模拟）激光雷达数据估计了海洋表面的临近预报，就可以在取决于初始化周期和重建中解析的波数的时间窗口内对船舶前方的预期波浪进行预测。然后可以对另一个预测窗口重复该过程，等等。然而，为了重建恶劣的海况，海面表示中必须包含非线性效应。这里，这是通过使用高效的拉格朗日波涛模型来表示海洋表面来完成的。
We report on the development of free surface reconstruction algorithms to predict ocean waves, based on spatial observations made with a high-frequency Flash light detection and ranging camera. We assume that the camera is mounted on a vessel, in a forward looking position, and is pointing at some distance ahead of its path yielding a sample of spatiotemporal wave elevation data. Because of the geometry, the density of measurement points gradually decreases (i.e., becomes sparse) with the distance to the camera. Free surface reconstruction algorithms were first developed and validated for linear 1-D and 2-D irregular surface models, whose amplitude coefficients are estimated on the basis of minimizing the mean square error of simulated surface elevations to measurements, over space and time (for a specified time initialization period). In the validation tests reported here, irregular ocean surfaces are generated on the basis of a directional Pierson-Moskowitz or Elfouhaily spectrum, and simulated LIDAR data sets are constructed by performing geometric intersections of laser rays with each generated surface. Once a nowcast of the ocean surface is estimated from the (simulated) LIDAR data, a forecast can be made of expected waves ahead of the vessel, for a time window that depends both on the initialization period and the resolved wavenumbers in the reconstruction. The process can then be repeated for another prediction window, and so forth. To reconstruct severe sea states, however, nonlinear effects must be included in the sea surface representation. This is done, here, by representing the ocean surface using the efficient Lagrangian choppy wave model .