Mission-based radar optimisation via Automated Scenario Recognition

Mission level optimization of a radar system or network of cooperating radars requires meta-knowledge on the environment, the targets and their behavior and about preferences of the operator using the system. Many of these parameters cannot be measured directly by the radar and usually depend on the 'man-in-the-loop to change settings manually or via relatively simple rule-based automation. This paper describes a novel approach towards a learning ability which allows a radar manager to extract knowledge from innovations in the air picture. This knowledge can be applied to mission-based optimization of that radar or system of radars, thereby bringing a true cognitive radar a step closer.