Adaptive Kalman Filter-based Estimator with Sea Trail Data to Calculate Ship States in Complex Navigation Conditions

Abstract

With the progress of innovative technologies, ships in future with different autonomy levels are anticipated to enter the realm of maritime transportation. As a result, the scenarios of multi-ship encounters at sea can become more complex and the risk of potential collisions can be difficult to elevate. To support navigation safety and guarantee the required situation awareness level, it is therefore essential to acquire ship navigation states with a greater degree of precision. The Kalman Filter (KF)-based techniques are one of the popular approaches for deriving the ship navigation state by merging the prior estimates from physics-based models with measurements from onboard sensors. However, many KFbased estimates are calculated by assuming constant system and measurement uncertainties during the iterative process. In this study, an adaptive tuning mechanism in the KF-based techniques is utilized to estimate ship navigation states. This approach enables the estimation processes to skillfully reduce both system and measurement noises estimations. Consequently, it results in the generation of smoother and more responsive estimates of the respective vessel states, particularly when confronted with variations in rudder orders or encountering abnormal measured positions.