Sensor Management for Covert Anti-Drone Tracking

Sammanfattning

The presence of adversarial or rogue drones is becoming increasingly common and problematic in military and civilian contexts. In the military domain, rogue drones are often used to scout covert positions and destroy expensive radar equipment. Restricted airspace around critical civilian infrastructure, such as airports, may also be violated, resulting in closures and delays. To combat this growing threat, this thesis, conducted at the Swedish Defence Research Agency (FOI), explores how sensor management can be applied to multi-target tracking (MTT) using radars to minimize the risk of exposing a protected covert position and losing expensive equipment. Four different drone flight scenarios were realistically simulated. Simulated detections from two radars and two radio frequency (RF) sensors were fused using an extended Kalman filter (EKF) by global nearest neighbor (GNN) and joint probabilistic data association (JPDA) trackers. A novel sensor management solution is suggested, involving splitting a reward function into separate interpretable components. Each component is designed to capture different aspects, in this case radar use and track quality. The proposed sensor manager uses breadth-first search (BFS) to maximize the reward function, thereby balancing track quality with radar use. The results show that the proposed method reaches almost as good performance as the non-covert baseline of always using the radars, while on average decreasing active radar use by ∼79–85 %.