Funded by the EU, the CASSATA project, with a consortium of 27 organizations from 9 countries, including defense groups, academic labs, research organizations, and SMEs, aims to advance covert sensing technologies for defense applications by developing and testing multi-modal sensor systems. The concept of CASSATA relies on exploiting (1) optronics, (2) acoustics/seismic sensing, (3) RF and radar systems, and (4) adaptive and collaborative sensing to enhance European armed forces’ target acquisition and reconnaissance capabilities. CASSATA will implement innovative technologies to improve the covertness of optronic sensors as well as their capabilities of detection, tracking, classification and identification. Both acoustic and seismic sensors will provide a wide area surveillance system which ensures the long-range detection and classification of targets in all environments where noise can be produced. The implementation of innovative RF and radar technologies will ameliorate the detection and tracking of targets of different size and speed in diverse environments. Utilization of multiple sensors of various types and characteristics requires a collaborative sensing environment that allows the adoption of new generation data fusion and Artificial Intelligence (AI) techniques, which maximize the ISR capabilities of the sensing systems. The project’s methodology includes studies to evaluate new solutions and advance computing techniques, design activities based on the studies and implementation and testing of the designed components and systems tailored to end-users needs.

MKLab is responsible for the scientific and technical management of the CASSATA project. Hence, this role ensures that the scientific and technological objectives of the project are covered. In addition, the Lab will deliver a feasibility study about a potential CASSATA architecture by collecting and assessing all technical specification of the involved sensory systems. To this end, acting as a WP leader, a structural representation of the data exchange will be delivered developing an interoperability layer of covert sensing components. Finally, AI-model compression techniques will be designed and developed in order to minimize the extensive requirements for the resources needed for optronic-based detections.





  • Ioannidis Konstantinos
  • Kintzios Spyridon
  • Papadopoulos Symeon
  • Vrochidis Stefanos