The use of the `ship as a wave buoy analogy' (SAWB) provides a novel means to estimate sea states, where relationships are established between causal wave properties and vessel motion response information. This study focuses on a model-free machine learning approach to SAWB-based sea state estimation (SSE), using neural networks (NNs) to map vessel response spectral data to statistical wave properties for a small uninhabited surface vessel. Results showed a strong correlation between heave responses and significant wave height estimates, whilst the accuracy of mean wave period and wave heading predictions were observed to improve considerably when data from multiple vessel degrees of freedom (DOFs) was utilized. Overall, 3-DOF (heave, pitch and roll) NNs for SSE were shown to perform well when compared to existing SSE approaches that use similar simulation setups. One advantage of using small vessels for SAWB was shown as SSE accuracy was reasonable even when motion responses were low (in high-frequency, low wave height sea states). Given the information-dense statistical representation of vessel motion responses in spectral form, as well as the ability of NNs to effectively model complex relationships between variables, the designed SSE method shows promise for future adaptation to mobile SSE systems using the SAWB approach.
翻译:使用`船舶作为波浪浮标类比'(SAWB)为估计海况提供了一种新颖的手段,在海况中,因波性质和船只运动反应信息之间建立了关系,这项研究的重点是利用神经网络绘制船只反应光谱数据,用于为小型无人居住的海面船只统计波特性,利用神经网络绘制船只反应光谱数据,以绘制船只反应光谱数据,作为海浪波的统计波比比(SAWB),这为估计海况提供了一种新颖手段,在海浪平均波幅和波头预测的准确性得到观察,以便在利用来自多个船舶自由度的数据(DOFs)建立关系时,对海浪平均波波波幅和波头预测的准确性得到大大改善。总体而言,3-DOF(Have、投放和滚动)SISSE的NNF(H)、对使用类似模拟配置的SSSE方法的现有方法,显示,使用小型船舶对SIS的模型系统进行有效调整的能力,显示SISSIS的模型系统对未来前景性变的模型系统之间,一个好处是合理的。