U.S. Army tests AI-guided drone killer at Fort Hood
AI Analysis
The U.S. Army successfully tested an AI-guided counter-UAS system, the RIwP, at Fort Hood, demonstrating the ability to retrofit existing weapon stations to engage drones in under three seconds. This system, developed by Moog, Echodyne, and Picogrid, utilizes AI-powered radar to identify and target Group 1-3 UAS threats. The test highlights a cost-effective approach to C-UAS defense by upgrading existing assets rather than procuring entirely new systems.
Key Takeaways
- Moog, Echodyne, and Picogrid demonstrated the RIwP C-UAS system at Fort Hood.
- The RIwP retrofits existing Army turreted weapon stations with radar, edge computing, and AI targeting.
- The system engaged drone threats (Group 1-3 UAS) in under three seconds during live fire exercises.
- Echodyne’s EchoShield radar uses machine learning for drone classification and precise targeting data.
- The RIwP offers a cost-effective solution by upgrading existing hardware instead of requiring new vehicle purchases.
Why It Matters
The proliferation of low-cost drones, as seen in Ukraine, presents a significant threat to conventional military assets. This retrofit system addresses the cost asymmetry by providing a rapid and affordable defense against UAS threats, protecting valuable platforms. The success of this program could lead to widespread adoption across the Army’s vehicle fleet, significantly enhancing its C-UAS capabilities.
U.S. Army tests AI-guided drone killer at Fort Hood
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U.S. Army tests AI-guided drone killer at Fort Hood
By Emily Ryan Miller
May 22, 2026
Modified date: May 22, 2026
Courtesy photo
Key Points
- Moog, Echodyne, and Picogrid demonstrated a retrofit C-UAS system at Fort Hood, Texas in late March, engaging drone threats in under three seconds.
- The RIwP platform combines Echodyne's EchoShield radar and AI targeting to upgrade existing Army turreted weapon stations against Group 1-3 UAS threats.
At Fort Hood, Texas, in late March, engineers and soldiers ran live fire at drones and watched a retrofitted U.S. Army weapon station kill them in under three seconds. The exercise, called Operation Condor Rebirth, brought together Moog, Echodyne, and data platform company Picogrid to demonstrate that existing turreted weapon stations, the kind already mounted on vehicles across the U.S. Army’s fleet, can be transformed into capable counter-drone systems by bolting on a radar, an edge computer, and an AI targeting module rather than buying entirely new hardware.
Small unmanned aircraft systems, ranging from commercial quadcopters modified to carry grenades to purpose-built first-person-view kamikaze drones, have become a defining feature of modern land combat. Ukraine has seen thousands of such strikes against armored vehicles, logistics convoys, and infantry positions, and adversaries including Iran, Russia, and various non-state actors have invested heavily in scaling cheap drone production precisely because the cost asymmetry is so favorable. A $500 drone destroying a $4 million armored vehicle is a trade any attacker will accept repeatedly. Defending against that exchange requires systems that can engage threats quickly and at a cost that doesn’t bankrupt the defender.
The Moog Reconfigurable Integrated-weapon Platform, known as RIwP, addresses that problem not by replacing what soldiers already have but by upgrading it. The platform packages an edge computer and Echodyne’s EchoShield radar with cabling designed to connect with any existing U.S. Army turreted weapon station, meaning the retrofit can be applied across a wide range of vehicles without requiring a new vehicle purchase or a lengthy integration program. Echodyne describes EchoShield as a medium-range radar built on a commercial off-the-shelf design with industry-standard interfaces, which in practice means it can feed targeting data directly to other systems without bespoke software bridges. The radar uses machine learning models for classification, distinguishing between drone types and configurations, and generates precise location data that the AI targeting system then converts into a firing solution.
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What the Fort Hood exercise demonstrated was how well that chain functions under pressure. The team tested Group 1 through Group 3 UAS threats, a classification covering drones from under five pounds up to r