Estimation of Sea State Parameters from Onboard Real Ship Motions Using an Adaptive Kalman Filter

Abstract

Accurately estimating sea state parameters is crucial for ship safety and efficiency. The objective of this paper is to study the applicability of the Adaptive Kalman filter (AKF) to estimate sea state parameters—significant wave height, peak period, and relative mean wave direction—using onboard ship motion measurements. The main idea is to assess the performance of this method under real-world conditions including varying ship forward speed and heading and noisy measurements. In this study, data recorded from onboard the United States Coast Guard Cutter (USCGC) STRATTON is considered for testing the method. The method's performance is evaluated by comparing the estimated sea state parameters to those obtained from the Copernicus hind cast model. The obtained results show the AKF's capacity to estimate sea state parameters under real-world conditions, such as variable forward speeds and potential sensor and model inaccuracies.