| OBJ1 | Follow a User-Centric and Agile approach. | Ai4CUAV project will follow a User-centric and Agile approach, according to an iterative process where each iteration (or small step) requires feedback from the domain experts as input for making small adjustments on the requirements. The functionalities will be verified by means of specified test procedures. Finally, test environments will be defined, which will be virtually simulated and/or live test flights to allow focus experiments depending on the nature of the investigated content. |
| OBJ2 | Build a shared database of RF UAV and EO/IR signatures as training data and test set. | The project will build a shared database by collecting RF and EO/IR signatures of different types of drones, which can be used as training data and test set. This database will allow to compare different detection and classification AI-based algorithms (i.e. ML, DL, CNN, ..). |
| OBJ3 | Develop an “AI-framework” of AI-based algorithms to improve the Threat Evaluation of the anti-drone systems. | The project will develop novelty AI-based algorithms (i.e. ML, DL, CNN, ..) for the detection and classification of killer-drones base on: 1) Radar signatures (signals) 2) EO/IR signatures (images) 3) Killer-drone trajectories |
| OBJ4 | Improve the ANTI-DRONES prototype through the integration of the AI-framework and demonstrate it in realistic environments. | Ai4CUAV will improve the ANTI-DRONES prototype by: 1) revising the ANTI-DRONES architecture and making it robust and secure 2) implementing missing features 3) integrating the AI-framework in the ANTI-DRONES architecture 4) fixing bugs and verifying 5) produce documentations |
| OBJ5 | Impact, facilitate take-off and approach to market. | Maximise the impact of Ai4CUAV through the active dissemination to a wider audience and the preparation for the exploitation of the project’s results as a successful product. |
