
The proliferation of small unmanned aerial systems has fundamentally altered the threat landscape. These platforms are low-cost, difficult to detect, and increasingly autonomous, enabling adversaries to conduct surveillance and attacks with minimal signature.
This mission area prioritizes advanced sensing, characterization, and decision support, enabling rapid detection-to-decision timelines against sUAS threats. The approach integrates quantum-inspired optimization, reservoir computing, and multi-modal sensing to identify and classify targets in complex environments.
Rather than focusing on kinetic defeat mechanisms, this effort delivers a sensor-to-decision architecture that enhances situational awareness and enables operators to act with speed and confidence. The system is designed for on-the-move operations, supporting maneuver units and fixed installations alike, and provides actionable outputs for integration with existing countermeasure systems.