Tracking Vessel Activities with AIS Data using an Adaptive Extended Kalman Filter

Accurate vessel trajectory prediction is critical for ensuring effective maritime navigation and safety. This paper presents a sophisticated tracking approach that leverages the Adaptive Extended Kalman Filter (Adaptive EKF) to process raw Automatic Identification System (AIS) data, resulting in a significant improvement in both the accuracy and reliability of vessel position estimates. The Adaptive EKF is particularly effective at mitigating the noise inherent in AIS measurements, which often arises from signal interference, environmental factors, or equipment inaccuracies. By refining these noisy measurements, the Adaptive EKF provides more precise estimates of a vessel's position, velocity, and heading, which are essential parameters for safe and efficient navigation.