  Autonomous landing of UAVs in high sea states requires the UAV to land
exclusively during the ship deck's "rest period," coinciding with minimal
movement. Given this scenario, determining the ship's "rest period" based on
its movement patterns becomes a fundamental prerequisite for addressing this
challenge. This study employs the Long Short-Term Memory (LSTM) neural network
to predict the ship's motion across three dimensions: longi-tudinal,
transverse, and vertical waves. In the absence of actual ship data under high
sea states, this paper employs a composite sine wave model to simulate ship
deck motion. Through this approach, a highly accurate model is established,
exhibiting promising outcomes within various stochastic sine wave combination
models.
