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.

This paper discusses the application of resilience engineering principles by shipborne navigation systems. As a technological system, the ship navigation system comprises all the communication and navigation equipment required to operate a ship. If examined as a socio-technological system, one has to additionally consider the use of the ship navigation system by the bridge teams in order to perform the nautical profession in terms of safe and efficient ship navigation, taking into account environmental information received by communication. The first part of this work discusses the theoretical background of resilience engineering and situation awareness. Case studies are used to illustrate under which conditions the application of resilience principles may result in an improvement of the operational reliability. With the help of simulations, it is shown that a sub-optimal implementation and utilization of resilience principles may decrease the robustness of the technical ship navigation system, as well as the reliability and adaptability of the ship navigation system in use. The examples illustrate once again that monitoring is one of the four cornerstones of resilience: anticipating, monitoring, learning, and responding. This is due to the effectiveness of most resilience principles depending on the availability and trustworthiness of situational information in relation to system status and environmental conditions, irrespective of whether the generation and use of the situational information is machine-made or human-made. Therefore, the establishment of situation awareness is an essential accompanying functionality to be considered in design, operation, and use of resilient systems.

Endeavours to have a reliable and robust maritime traffic situation assessment are today leading to exploit the improved sensor resolution technology of radars. In such concerned tracking scenarios, multiple noisy scattered measurements arise from targets of interests surfaces at each observation step. In this paper, we present a special version of the Multiplicative Error Model-Extended Kalman Filter (MEM-EKF*) approach to recursively track the orientation and kinematics of targets having known physical dimensions from a maritime- and radar-based dataset. Altogether, we also introduce and thereby propose an open dataset part of a marine radar benchmark dedicated to extended target tracking to the community. The benefits and performance of the proposed approach adopted on our dataset are discussed taking into consideration the non-ideal but nonetheless prevailing effects of the quotidian marine radar such as beam spreading and false negatives. As compared to its original version, the proposed approach proved to be more computationally efficient.

We systematically evaluate the high‐latitude Joule heating of the recently released version 3.0 Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE‐GCM) by comparison to EISCAT incoherent scatter radar measurements. The model performance is examined using normalized root mean square deviations derived from test runs driven by different convection patterns from empirical and data‐assimilated models. The following features are revealed: (a) Data‐assimilated geomagnetic forcing improves the agreement between modeled and EISCAT‐derived Joule heating rates by 8%, 28%, and 54% for low, moderate, and high geomagnetic activity. (b) Increasing model grid resolution from 2.5° to 1.25° leads to 20% higher Joule heating rates. (c) AMIE‐driven runs better reproduce the magnitude of the Joule heating rates, AMGeO‐driven runs the vertical profile. (d) Internal model time step resolution has no effect on the Joule heating rates.

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.