2020-2023    H2020

BorderUAS – Semi-autonomous border surveillance platform combining next generation unmanned aerial vehicles with ultra-high-resolution multi-sensor surveillance payload



The project combines for the first time a multi-role lighter-than-air (LTA) unmanned aerial vehicle (UAV) with an ultra-high resolution multi-sensor surveillance payload supporting border surveillance as well as search & rescue applications, and specifically rough terrain detection. The sensor payload will include synthetic aperture radar (SAR), laser detection and ranging (LADAR), shortwave/longwave infrared (SWIR/LWIR) and acoustic cameras for direct target detection, as well as optical and hyperspectral cameras for indirect detection (via vegetation disturbance). The project will use the ground-based infrastructure of border police units (command & control centres), innovative data models (to identify illegal crossing patterns and preferred routes) and advanced audio/video analytics and storage (to provide additional detection capabilities). The technology concepts will be validated in the field by 6 border police units (Greece, Bulgaria, Romania, Moldova, Ukraine, Belarus) covering 3 major illegal migration routes into Europe (Eastern Mediterranean, Western Balkan and Eastern Borders Routes), which represent 58% of all illegal border crossings detected and are also the most used for smuggling of drugs, weapons and stolen vehicles. The combined solution will provide high coverage, resolution and revisit time with a lower cost (4 EUR/kg/hr) than satellites and higher endurance (100 kg payload for 12 hours) than drones. Based on the field trial results, the consortium expects to develop a solution that can be deployed further by European border polices after project completion. The project will also involve the contribution of NGOs working with illegal migration and human right protection issues, as well as regulatory experts dealing with the ethics and privacy requirements of border surveillance solutions


Contact: iva.salom@pupin.rs