ARESIBO aims at improving the efficiency of the border surveillance systems by providing the operational teams and the tactical command and control level with an accurate and comprehensive information. The pillars of research in ARESIBO are three-fold: 1. Set-up a complete configuration at tactical and execution level to optimise the collaboration between human and sensors (fixed and mobile), 2. Improve situation awareness by enhancing the understanding of the situation through adapted processing of sensor data, correlation between heterogeneous data and information and creation of knowledge through deep learning techniques and 3. Create a situation awareness capability at C2 level that will combine reports on previous missions, real time situation understanding and threat analysis for future actions. This capability will be used to optimise the operations (teams deployment and sensor positioning) as well as an online briefing tool for the teams that will be able to access to the results of the previous missions while in the field. ARESIBO integrates research activities in the domain of 1. surveillance platforms (air, ground, surface, underwater) to optimise the collaborative capabilities of the platforms and their positioning (between themselves and with the teams), 2. Sensor processing to interpret, fuse and correlate all the data to produce information and knowledge and 3. Augmented reality techniques to elaborate and provide to the operators a situation awareness picture which is fit for their missions (minimum information for maximal understanding) both as team level and tactical C2 level. The ARESIBO system will be developed incrementally during the 3 years with two major versions that will lead to sub-versions for land and maritime borders. The system will be tested and assessed in 1. a controlled environment enabling testing at any time without pre-requisite authorisations and 2. in real conditions in Finland, Greece, Romania and Portugal for the 2 versions.
|Effective start/end date||1/05/19 → 30/04/22|
- Detection technology
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