Next Generation Mission Critical Control Centre solutions for sustainable decision support
Current maritime control centers are heterogeneous systems with numerous legacy components. Due to coastal wide sensor networks the data load has increased exponentially and current system architectures are no longer sufficient to process that data efficiently. MARNG researches new concepts that will offer interoperability, safety, security, and performance. New data analysis methods, derived from the big data domain, will drive mission critical decision support for the maritime domain awareness, reducing disaster risk dramatically.
MARNG aims to
- devise concepts for an integrative system architecture allowing for interoperability of heterogeneous sub-systems and legacy systems in a safe and secure way
- develop novel prediction algorithms for improved collision warning based on physical modes taking into account real-time data about a vessel’s state
- develop novel algorithms for the prediction of the mid-term behavior of vessels and for the automatic and real-time detection of unusual situations
- implement a lab demonstrator allowing to demonstrate, verify and validate the concepts developed
This project is funded by the Austrian Federal Ministry for Transport, Innovation and Technology (bmvit) within the programme “IKT der Zukunft” under grant 861258.
Graser, A., & Widhalm, P. (2018). Modelling Massive AIS Streams with Quad Trees and Gaussian Mixtures. In: Mansourian, A., Pilesjö, P., Harrie, L., & von Lammeren, R. (Eds.), 2018. Geospatial Technologies for All: short papers, posters and poster abstracts of the 21th AGILE Conference on Geographic Information Science. Lund University 12-15 June 2018, Lund, Sweden. ISBN 978-3-319-78208-9.
Graser, A., Schmidt, J. & Widhalm, P. (2018). Predicting trajectories with probabilistic time geography and massive unconstrained movement data. In: GIScience 2018 Workshop on Analysis of Movement Data (AMD’18), 28 August 2018, Melbourne, Australia