This paper aims at estimating both unidirectional and multi-directional waves from noisy measured ship motion data, with a focus on the inclusion of the vessel's forward speed to reflect real-world operating conditions. The technique is based on an Adaptive Kalman Filter for estimating wave elevation and wave spectrum parameters, including significant wave height, peak period, and wave direction. The proposed method was tested using simulated ship motion data, and its performance was evaluated by comparing the estimated wave spectrum with reference values used in the simulation model and with results from a widely used baseline frequency domain approach. The results demonstrate that the method effectively estimates the wave spectrum in a short measuring window with a reasonable degree of accuracy when accounting for varying forward speed, indicating strong potential for real-time wave estimation to aid in improving navigation, safety, and operational efficiency. ... Read more

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. ... Read more