  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.
