Improved Fusion of AIS Data for Multiple Extended Object Tracking

In maritime situational awareness, the Automatic Identification System (AIS) is a vital source of information. Recent work has explored the fusion of AIS information and exteroceptive measurements to improve maritime target tracking performance, also for extended object tracking. However, in extended object tracking, the discrepancy between the center of the ship and the position reported by the AIS system is no longer negligible and is a source of systemic bias, which can degrade tracking performance. In this paper, we introduce a method for estimating this discrepancy based on AIS information and the estimation provided by the Gaussian process target model from the exteroceptive sensor data. We use this method combined with an extended object Poisson multi-Bernoulli mixture (PMBM) filter to perform multiple extended object tracking. We also introduce a specific method for initialization of targets using AIS measurements in this filter. We validate the proposed method with LiDAR and AIS data, collected from an inland waterway in Belgium. The results show that compensating for the bias in this manner results in better tracking performance, primarily due to better initialization of new targets.