This work presents a novel approach for integrity monitoring of AIS data. Currently, the AIS is a valuable source for maritime traffic situation assessment but not suited for collision avoidance, as it is prone to failures and not capable of indicating the level of data integrity. To tackle this, an EKF was designed to track vessel trajectories, which allows for failure detection based on residual monitoring. For the latter, two methods for hypotheses testing were implemented, namely chisquared and GLR tests. In addition, the IMM framework was adopted for mixing the state estimates of two different process models, the CV and CTRV. The designed filter will be validated on behalf of simulated and real-world AIS data.
is a research fellow at the German Aerospace Center (DLR), in the Department of Nautical Systems of the Institute of Communications and Navigation. In 2010 he received his Diploma degree in electrical engineering from the University of Technology Ilmenau in Germany. Before joining DLR in 2015, he worked in the field of SatCom-On-The-Move and Over-The-Air RF testing at the Fraunhofer Institute for Integrated Circuits. Currently, his research is focused on cooperative maritime traffic situation assessment being particularly interested in distributed sensor fusion and target tracking. Paweł Banyś holds a master's degree in finance and banking and an engineer's degree in geodesy and cartography. Between 2001 and 2010 he was employed at different IT companies as network and Linux administrator. He also cooperated with the Maritime University of Szczecin on a vessel traffic safety project. Since 2010 he has been working at the DLR Department of Nautical Systems in the field of AIS and maritime traffic systems. Julian Hoth is a research associate at the German Aerospace Center (DLR) in the Department of Nautical Systems. He received a master's degree in mechanical engineering and a doctoral degree from the University of Duisburg-Essen, Germany. Before joining DLR in 2016, Julian worked in the area of underwater navigation and underwater imagery at the Chair of Mechanics and Robotics of the University of Duisburg-Essen. His current research is focused on radar target detection and tracking. Frank Heymann received a PhD in physics from the University of Bochum in collaboration with the European Southern Observatories (ESO). From 2010 until 2012 he has worked in the field of active galactic nuclei in astrophysics as a postdoctoral researcher at the University of Kentucky. In 2012 he joined the DLR Institute of Communications and Navigation as a research associate in the field of maritime navigation. Since 2014 he is the group leader of the group Traffic Systems in the Department of Nautical Systems.
Collision avoidance is one of the high-level safety objectives and requires a complete and reliable description of the maritime traffic situation. The radar is specified by the IMO as the primary sensor for collision avoidance. In this paper we study the performance of multi-target tracking based on radar imagery to refine the maritime traffic situation awareness. In order to achieve this we simulate synthetic radar images and evaluate the tracking performance of different Bayesian multi-target trackers (MTTs), such as particle and JPDA filters. For the simulated tracks, the target state estimates in position, speed and course over ground will be compared to the reference data. The performance of the MTTs will be assessed via the OSPA metric by comparing the estimated multi-object state vector to the reference. This approach allows a fair performance analysis of different tracking algorithms based on radar images for a simulated maritime scenario.
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