FAAD
Forward Area Air Defense Command and Control. Photo: Northrop Grumman

Northrop Grumman has integrated an artificial intelligence feature into its Forward Area Air Defense (FAAD) system, enhancing counter-drone operations for the US and its allies. 

Leveraging years of live fire data, successful tests conducted this year at the US Army’s Yuma Proving Ground in Arizona demonstrated the new AI-driven Advanced Battle Manager’s (ABM) capability to counter complex drone swarms with a single click on a mobile tablet.

This upgrade also enhances the maneuverability among US forces, allies, and coalition partners. 

Enhanced Threat Response

With the new AI feature, the FAAD command and control software can receive and process information from multiple sensors and generate engagement plans in under a quarter of a second.

Then, it analyzes threats and recommends optimal real-time weapon-target pairings. 

As this AI-driven ABM continuously monitors the battlespace and adjusts plans as necessary, it helps warfighters manage multiple targets effectively during high-stress situations.

“This critical AI enhancement will create a streamlined and intuitive engagement plan giving service members more time to save lives with a single click,” Northrop Grumman VP and General Manager of Global Battle Management and Readiness Kenn Todorov said.

“Northrop Grumman and the US Army’s collaboration in innovation ensures the combat-proven FAAD system remains at the forefront of C-UAS technology,” he added. 

You May Also Like

Turning Data Into Battlefield Insight: CGI and Vantor Team Up on AI Spatial Intelligence

The effort will merge CGI’s AI-driven analytics, edge computing, and visual processing with Vantor’s spatial intelligence, geo-positioning, and GNSS-resilient navigation tech, including its Tensorglobe platform and Raptor system.

USSOCOM Invests $99.7M in On-Site AI for Disconnected Ops

Bridge Defense’s portable GPU-powered systems will push advanced AI analytics to forward-deployed forces.

US Army Bets $2M on AI to Spot Anomalies in Sensor Data

The 18-month effort will convert video, audio, imagery, and other sensor feeds into structured data while helping identify patterns across sources.