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    • ARESIBO

    2020-04-15

    GRANT NUMBER: 833805

    DURATION: 1 May 2019 - 30 April 2022

    LINKS: CORDIS, PROJECT WEBSITE, TWITTER, LinkedIn, NEWSLETTER

    FOCUS AREA: artificial intelligence, augmented reality, border surveillance, command and control, situational awareness, unmanned systems, sensor processing, surveillance platforms, mission system

    ARESIBO is an innovative surveillance system for improved situational awareness in the border security domain. The system addresses the need to provide enhanced and integrated situational awareness to the operational and C2 personnel. It focuses on the operational and tactical layers by covering a wide range of border operational tasks and coast guard functions including surveillance, tasking, patrolling, situational awareness, analysis of potential threats, search and rescue activities and joint planning of field operations.

    The top priority for the project is to use new fusion techniques, Artificial Intelligence and Augmented Reality in order to ensure a seamless transmission of information and to build intuitive interfaces for field operations and C2 centres. The system will acquire a more accurate understanding of the situation by filtering a large amount of information collected from multiple sources. 

    ARESIBO considers the use of all types of unmanned systems (UxV swarm intelligence) that can meet the operational requirements of different border security missions at the green and blue borders.

    Research topics:

    • optimise the collaborative capabilities of surveillance platforms for their integration into existing systems
    • sensor processing to interpret, fuse and correlate large amount of data to produce information and knowledge
    • Artificial Intelligence and Augmented Reality techniques to elaborate and provide to the operators a situation awareness picture which is tailored to their missions (minimum information for maximal understanding) both at team level and tactical C2 level

    This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 833805