Line-of-sight path-following control utilizing an extended Kalman filter for estimation of speed and course over ground from GNSS positions

Abstract

Path-following control systems for ships can be designed using both heading and course angle autopilots in conjunction with a proportional line-of-sight (LOS) guidance law. Ships are usually equipped with a gyrocompass from a safety perspective since magnetic compasses are susceptible to magnetic disturbances. Unfortunately, the gyrocompass is an expensive device, and smaller vessels and boats cannot afford to use this as the primary device for steering. An alternative solution is to compute the course over ground (COG) and speed over ground (SOG) from global navigation satellite systems (GNSS) and use these signals for feedback control. This article presents course autopilot design for path following and a five-state extended Kalman filter (EKF) to estimate the COG and SOG efficiently. Even though many algorithms exist for computation of the COG and SOG, it is advantageous to design an EKF since a state estimator can be extended to include other sensory systems such as Doppler Velocity Log (DVL), radar, attitude rate sensors, computer vision systems, etc. This is in contrast to proprietary systems that do not allow the user to modify the software. The convergence and accuracy of the EKF are significantly improved by using target-tracking models in combination with kinematic equations. A high-fidelity model of a MARINER class cargo ship is used in the path-following case study. From the simulation study, it can be concluded that the EKF successfully estimates the COG and the SOG from GNSS measurements during path following. The solution is remarkably robust and accurate, and when combined with a course autopilot, the need for a compass is eliminated during path following.