U.S. Army evaluates JLTV-based RAD counter-drone system

The U.S. Army is evaluating a low-cost counter-drone system developed by SAIC in collaboration with Kongsberg Defence and Aerospace, installed on a Joint Light Tactical Vehicle chassis during Project Flytrap in Lithuania.

The system, known as the Reconfigurable Air Defense system or RAD, is being tested as part of Project Flytrap, a counter-UAS exercise running from April 27 to May 31 as part of a series of linked exercises including Sword 26, Saber Strike, Immediate Response, and Swift Response, according to the Army’s statement on the evaluation.

The RAD system consists of a turret, radar sensor, and AI components that assist the crew in detecting and engaging enemy drones. Mounting the system on a JLTV chassis gives it the mobility to keep pace with maneuver forces rather than operating from a fixed position — a critical design choice for counter-drone capability that needs to protect units on the move rather than static installations.

The RAD system’s development partnership between SAIC and Kongsberg Defence and Aerospace brings together an American defense technology integrator with a Norwegian company that has been expanding its counter-UAS portfolio significantly in recent years. Kongsberg’s experience with remote weapon stations, fire control systems, and autonomous targeting brings directly relevant industrial expertise to a system that combines a stabilized turret with AI-assisted drone detection and engagement.

The JLTV, which entered Army service to replace the aging High Mobility Multipurpose Wheeled Vehicle in demanding operational roles, provides a platform capable of carrying the RAD system’s turret, sensor, and processing components across the kind of terrain that infantry and armored units actually operate in. Using an existing vehicle chassis rather than developing a purpose-built carrier reduces procurement cost and simplifies logistics, since the JLTV is already in the Army’s supply chain with established parts, training, and maintenance infrastructure. The RAD system’s description as a low-cost counter-drone solution is significant in a market where many air defense systems are priced at levels that make them impractical for widespread fielding at the unit level.

The one-way drone threat that the RAD system is specifically designed to counter has become the Army’s most pressing near-term air defense problem. One-way attack drones — kamikaze systems designed to fly to a target and detonate — have been employed at scale in Ukraine, where Russian forces have used them to strike vehicles, logistics nodes, personnel, and equipment across the depth of the battlefield. The threat is relatively inexpensive to generate in large numbers, difficult to intercept consistently with missile-based systems whose per-shot cost can exceed the cost of the drone being intercepted, and effective against the kind of targets that light and medium forces carry. A mobile, affordable counter-drone system that a company or battalion can own and operate organically addresses that threat at the echelon where it is actually encountered.

Project Flytrap, running concurrently with the larger exercise series in Lithuania, is specifically designed to integrate counter-unmanned systems, AI-enabled command and control, and live data networks while informing future Army requirements and doctrine, according to the Army’s description. Testing the RAD system within that framework — alongside other counter-drone technologies, in an environment that also includes offensive drone operations — gives evaluators the opportunity to assess how the system performs as part of a layered defense architecture rather than in isolation. A counter-drone system’s effectiveness is not just a function of its individual technical performance but of how well it integrates with the detection systems, command networks, and other defeat mechanisms operating around it.

The AI components in the RAD system address the operator workload challenge that has historically limited the effectiveness of vehicle-mounted anti-aircraft systems against small, fast-moving drone targets. A human operator manually tracking a one-way attack drone through an optical sight while simultaneously managing vehicle movement, communications, and situational awareness is performing a task that degrades rapidly under the cognitive demands of a combat environment. AI-assisted detection and tracking shifts the burden of acquiring and following the target to the system, allowing the operator to focus on threat prioritization and engagement decisions rather than the mechanics of keeping a sight picture on an incoming drone.