Discrete Kalman Filter and Linear Regression Comparison for Vessel Coordinate Prediction

Automatic Identification System (AIS) is one of the most prominent systems for monitoring vessel activity. Although significant advances in AIS coverage have been achieved in recent times, a vessel will typically still experience gaps in reception within its voyage. These gaps can vary from a few seconds to multiple hours depending on the route. A typical approach when dealing with these gaps when monitoring a vessel's voyage is to move from a pointal domain to a trajectory domain using a trajectory prediction algorithm. In this paper, we compare the performance of two trajectory prediction algorithms being (1) the Discrete Kalman filter (DKF) and (2) the Linear Regression Model (LRM). It is postulated that the DKF, with its added complexities in initiating the relevant parameters does not yield significant performance advantages over using a computationally simpler LRM.