Army Tests AI-Based Multisensor Tech for Explosives Detection

Army Tests AI-Based Multisensor Tech for Explosives Detection
AI-Based Multisensor

The U.S. Army has demonstrated a machine learning-based sensor technology designed for fixed-wing aircraft that works with synthetic aperture radars to detect explosives from a designated safety point.

As part of the 15-month Phase I demonstration, the Army Combat Capabilities Development Command and the Army Research Laboratory (ARL) assessed the multisensor technology’s capacity to use artificial intelligence and ML to collect real-time target data, the service branch said Wednesday

The team used airborne SARs, electro-optical and infrared radars, a  junction detection radar and Light Detection and Ranging technologies for ground vehicles and small unmanned aerial vehicles.

Detection algorithms used for the exercise involved geo-referencing and pixel-alignment techniques to enable threat detection through an augmented reality approach.

Lt. Col. Mike Fuller, program manager at the Defense Threat Reduction Agency, said the team used side-by-side comparisons of multiple modalities during the assessment to maximize the probability of threat detection while minimizing false alarms.

For Phase II, the Army intends to downselect a sensor system ahead of a final demonstration on various types of terrain. DTRA funds the three-year effort through the Blood Hound Gang Program.

You may also be interested in...

Gen. John Hyten

Gen. John Hyten: MDA Must Realign R&D Priorities With Core Mission

Gen. John Hyten, vice chairman of the Joint Chiefs of Staff, has said the Missile Defense Agency (MDA) must focus on next-generation concepts and align their capabilities with the core mission. He added that the MDA must revitalize collaboration with the Joint Requirements Oversight Council to inform requirements for air and missile defense.