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

2025-11-26

AI Summary

Real-time sea state estimation conventionally relies on a priori wave-ship transfer functions, limiting adaptability to scenarios where such functions are unknown or time-varying. Method: This paper proposes a novel joint estimation framework for wave spectra and ship hydrodynamic parameters. It introduces a pseudo mass-spring-damper equivalent model, treating wave excitation as a time-varying input—departing from the conventional assumption of constant external forcing. Based on this dynamic model, statistically consistent process noise covariance is derived, and square-root cubature Kalman filtering enables robust multi-sensor data fusion. Estimation accuracy is quantified via the posterior Cramér–Rao lower bound. Results: Monte Carlo simulations and high-fidelity numerical experiments demonstrate that, without requiring prior transfer function knowledge, the proposed method achieves wave spectrum estimation accuracy comparable to benchmark methods assuming perfect transfer function knowledge. It significantly enhances practical applicability, robustness, and engineering deployability in realistic ocean environments.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements

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.

Problem

• Estimates sea state and vessel parameters without prior transfer functions
• Models wave-vessel system using pseudo mass-spring-dampers
• Evaluates estimator performance with Posterior Cramer-Rao lower bound

Innovation

• Joint estimation of sea state and vessel parameters without prior transfer functions
• Modeling wave-vessel system with pseudo mass-spring-dampers for dynamic input
• Implementing square root cubature Kalman filter for sensor data fusion