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.

Situation awareness is understood as a key requirement for safe and secure shipping at sea. The primary sensor for maritime situation assessment is still the radar, with the AIS being introduced as supplemental service only. In this article, we present a framework to assess the current situation picture based on marine radar image processing. Essentially, the framework comprises a centralized IMM-JPDA multi-target tracker in combination with a fully automated scheme for track management, i.e., target acquisition and track depletion. This tracker is conditioned on measurements extracted from radar images. To gain a more robust and complete situation picture, we are exploiting the aspect angle diversity of multiple marine radars, by fusing them a priori to the tracking process. Due to the generic structure of the proposed framework, different techniques for radar image processing can be implemented and compared, namely the BLOB detector and SExtractor. The overall framework performance in terms of multi-target state estimation will be compared for both methods based on a dedicated measurement campaign in the Baltic Sea with multiple static and mobile targets given.

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Objects look very different in the underwater environment compared to their appearance in sunlight. Images with correct colouring simplify the detection of underwater objects and may permit the use of visual simultaneous localisation and mapping (SLAM) algorithms developed for land-based robots underwater. Hence, image processing is required. Current algorithms focus on the colour reconstruction of scenery at diving depth where different colours can still be distinguished, but this is not possible at greater depth. This study investigates whether machine learning can be used to transform image data. First, laboratory tests are performed using a special light source imitating underwater lighting conditions, showing that the k-nearest neighbour method and support vector machines yield excellent results. Based on these results, an experimental verification is performed under severe conditions in the murky water of a diving basin. It shows that the k-nearest neighbour method gives very good results for short distances between the object and the camera, as well as for small water depths in the red channel. For longer distances, deeper water and the other colour channels, support vector machines are the best choice for the reconstruction of the colour as seen under white light from the underwater images.