Joint Estimation of Sea State and Vessel Parameters Using a Mass-Spring-Damper Equivalence Model

TL;DR

This paper introduces a novel method for real-time sea state estimation that jointly estimates sea state and vessel parameters without prior transfer function knowledge, using a mass-spring-damper model and advanced filtering techniques.

Contribution

It presents a new dynamic modeling approach with recursive wave excitation estimation, eliminating the need for prior transfer function data, and validates it through simulations and high-fidelity data.

Findings

Estimated wave spectrum matches traditional methods with known transfer functions.

The method effectively estimates vessel parameters alongside sea state.

Performance bounds confirm estimator efficiency.

Abstract

Real-time sea state estimation is vital for applications like shipbuilding and maritime safety. Traditional methods rely on accurate wave-vessel transfer functions to estimate wave spectra from onboard sensors. In contrast, our approach jointly estimates sea state and vessel parameters without needing prior transfer function knowledge, which may be unavailable or variable. We model the wave-vessel system using pseudo mass-spring-dampers and develop a dynamic model for the system. This method allows for recursive modeling of wave excitation as a time-varying input, relaxing prior works' assumption of a constant input. We derive statistically consistent process noise covariance and implement a square root cubature Kalman filter for sensor data fusion. Further, we derive the Posterior Cramer-Rao lower bound to evaluate estimator performance. Extensive Monte Carlo simulations and data from a high-fidelity validated simulator confirm that the estimated wave spectrum matches methods assuming complete transfer function knowledge.