2024A-OS2-2 Bayesian Estimation of Roll Damping Based on Onboard Measurements

抄録

This paper numerically demonstrates the Bayesian estimation of damping terms. In the Bayesian estimation framework, the posterior distributions are to be derived via a sampling-based approach. This study employes the Markov chain Monte Carlo (MCMC) based on the Metropolis-Hastings algorithm and the Transitional Markov chain Monte Carlo (TMCMC). A simple mass-spring-damper model is first utilized to compare the effectiveness of MCMC and TMCMC, followed by a numerical demonstration using a one degree-of-freedom (DOF) roll motion model for roll damping estimation.