The Pentagon wants faster decisions against small drones

The U.S. Department of Defense is turning to AI-enhanced target recognition in an effort to improve how troops, vehicles, and ships engage small drones. The project, known as C-UAS Close-In Kinetic Defeat Enhancement, centers on aided target recognition, or AiTR, using AI, machine learning, and computer vision to identify threats more quickly than a human operator can on their own.

The near-term objective is straightforward: shrink the time between seeing a drone and shooting it down. Just as importantly, the Pentagon wants systems that can distinguish real threats from non-threats such as birds, a problem that becomes more pressing as low-cost drones proliferate and visual clutter complicates engagement decisions.

The Defense Innovation Unit solicitation sets out a phased plan that begins with remote weapons stations and eventually reaches small arms carried by dismounted troops.

Phase one starts with the CROWS turret

The first phase is aimed at remote weapons stations, specifically the Common Remotely Operated Weapon Station, or CROWS, which is widely fitted to military vehicles. According to the solicitation, the system is intended to accelerate the engagement timeline with an initial focus on unmanned aircraft systems and a secondary focus on vehicular and man-sized targets.

Prototype systems must demonstrably improve the ability of current remote weapons stations to detect, track, and engage Group 1 and Group 2 drones, defined here as targets weighing 55 pounds and under. The solicitation says detection should occur at ranges greater than 600 meters, with engagement at a minimum of 100 meters. The system should also work against drones moving at speeds of at least 30 meters per second, or roughly 67 miles per hour.

Those numbers show the Pentagon is not looking for an abstract demo. It wants a system that operates under concrete performance thresholds relevant to real tactical engagements.