Maximum Likelihood Estimation of the Uncertain Dynamics of Small Catamaran Unmanned Surface Vehicles

Unmanned surface vehicles (USVs) operating in marine environments are often subjected to external disturbances, such as water waves and currents that might considerably affect system dynamics. Due to the complexity of existing tools used to capture their effects on system dynamics, they are often discarded or at most replaced by a mild form of noise. Indeed, traditional estimation methods often rely on complex procedures involving a battery of experiments conducted both in laboratory settings and outdoors in actual operating environments, followed by intensive hydrodynamics computations. Stochastic noise has been shown in the literature to better capture the uncertainties acting on marine vehicles. In this work, we propose a stochastic model to recreate the disturbances observed on small catamaran USVs moving at lower speed in their operating environment. A maximum likelihood method is proposed to identify the noisy dynamics of the USVs using limited amounts of experimental data gathered in the operating environment. Analytical expressions are derived which reduce the computational effort required to estimate model parameters. The proposed framework is shown to be effective at replicating the distribution of the noise and predicting the future trajectories of the USVs by a few time horizons in actual operating environments.