As autonomous navigation is being implemented in several areas including the maritime domain, the need for robust tracking is becoming more important for traffic situation awareness, assessment and monitoring. We present an online repository comprising three designated marine radar datasets from real-world measurement campaigns to be employed for target detection and tracking research purposes. The datasets have their respective reference positions on the basis of the Automatic Identification System (AIS). Together with the methods used for target detection and clustering, a novel baseline algorithm for an extended centroid-based multiple target tracking is introduced and explained. We compare the performance of our algorithm to its standard version on the datasets using the AIS references. The results obtained and some initial dataset specific analysis are presented. The datasets, under the German Aerospace Centre (DLR)’s terms and agreements, can be procured from the company website’s URL provided in the article.

Ground radar stations observing specific regions of interest nowadays provide detections in the form of point-clouds. This article focuses on a framework that consists of an elliptical multitarget tracker, referred to as Principal-Axes based Kalman Filter (PAKF)-based Joint Probabilistic Data Association (JPDA) (PAKF-JPDA), to enable maritime traffic monitoring. The framework touches on two major stages, target detection and target tracking. For the former, we employed a clustering approach and for the latter, we presented a data-association-based version of the PAKF tracker with an automatic track management functionality. The framework’s benefits are demonstrated when it is applied to the radar streaming in a harbor setting based on a homogeneous multisensor tracking system by comparing our results against their corresponding reference data with visualizations, including performance measures.

For almost two decades now, the automatic identification system (AIS) has contributed to increasing the safety of navigation at sea. In addition to that, the system can provide valuable data for surveillance of maritime traffic on a global scale. For reliable tracking the movement of vessels with AIS, it is important to correctly identify the craft. Nowadays, such identification is based entirely on the maritime mobile service identity (MMSI). Despite extensive research being done on the subject of anomalies detectable within the AIS, with emphasis on spotting and correcting erratic vessel movements, a possibility of accidental falsification of MMSI leading to incorrect track reconstruction of a vessel has so far been neglected. This study focuses on analysing hypothetical situations when the MMSI of an AIS transponder on board a vessel is altered within the transponder software and a valid AIS data message is sent with a false MMSI which accidentally belongs to a completely different vessel. Since such MMSI transformation leaves no traces, an assessment of a speed-based plausibility is additionally proposed to figure out how many MMSI based injections of false AIS track points can be detected.