Particle Filter Based Ship State and Parameter Estimation for Vessel Maneuvers

Abstract

Vessel states and parameters estimation is essential for maneuvering and collision avoidance. This study presents an application of particle filter (PF) algorithm to estimate vessel states and parameters. Particularly, to reduce the impact of the vessel’s underactuated property and complex environmental disturbance, the estimation process contains a kinematic curvilinear motion model that describes vessel’s motion. The estimated result can help navigators or ship onboard computers well comprehend the current vessel maneuvering condition. Besides, it can also serve as the necessary data source for vessel’s future trajectory prediction. Therefore, it can be integrated into vessel’s situation awareness (SA) module that supports safety navigation for both conventional and autonomous vessels.