Engagement-Zone-Aware Input-Constrained Guidance for Safe Target Interception in Contested Environments

A team of researchers has published a novel AI guidance framework designed for autonomous vehicles, like drones or missiles, to intercept a target while evading multiple defenders in contested airspace. The key innovation is moving beyond simplistic "maximum range" safety constraints. Instead, their system dynamically models each defender's actual "engagement zone"—the area where it can effectively intercept—and the vehicle's own physical actuator limits. This creates a more accurate and less conservative threat map.

To coordinate the dual objectives of reaching the target and avoiding all defenders, the team developed a smooth, switching guidance strategy. It uses a mathematical technique called a log-sum-exp operator to seamlessly combine the threat levels from multiple defenders into a single, scalable safety function. The AI pilot aggressively pursues the target when safe, then smoothly activates evasive maneuvers as it approaches any defender's engagement zone boundary. Crucially, the system is fully distributed, requiring only the attacker's own sensor data (relative positions) and no knowledge of the defenders' internal control commands.

Simulation results demonstrate significant practical advantages. Compared to traditional methods that use a fixed, conservative stand-off distance based on maximum defender range, this new engagement-zone-aware approach allowed for shorter, more direct flight paths. This directly translated to a reduced total interception time. The paper provides rigorous mathematical proofs guaranteeing that the system will both intercept the target and maintain practical safety—staying outside all defender engagement zones—while respecting the vehicle's own physical control limits throughout the entire maneuver.