Air Force Taps Pacific Defense and Perceptronics for AI-Driven RF Sensing

The U.S. Air Force has awarded a contract to Pacific Defense and its affiliate, Perceptronics, to develop airborne radio frequency (RF) situational awareness capabilities powered by artificial intelligence and machine learning. The initiative aims to modernize how aircraft detect and analyze signals in complex electromagnetic environments.

Under the contract awarded via the Air Force Life Cycle Management Center (AFLCMC), the partnership will develop advanced Electronic Warfare (EW) mission systems. These systems will utilize AI/ML algorithms specifically designed for the processing of RF emitters. To validate the technology, the capabilities will be integrated into an open architecture pod for flight demonstrations conducted in operationally representative environments. The project leverages Perceptronics' expertise in AI/ML alongside Pacific Defense's specialization in C5ISR solutions and the Modular Open Systems Approach (MOSA).

The Shift to Intelligent EW

This move comes as the U.S. Air Force increasingly integrates machine learning into its signals intelligence (SIGINT) and electronic warfare toolsets. In modern aerial combat, the ability to maintain situational awareness in contested electromagnetic environments is critical. Traditional RF sensing often requires significant manual analysis, but the transition toward software-defined, AI-driven sensing allows for the rapid identification of threats and more efficient coordination of communications.

Reducing Operator Cognitive Load

The primary significance of this contract lies in the automation of signal classification. By employing AI to handle the detection and analysis of complex signal environments, the Air Force can significantly reduce the cognitive load placed on pilots and operators. Automating these processes allows personnel to focus on high-level decision-making rather than the manual parsing of raw RF data, potentially increasing reaction speeds during engagements.

Future Integration

Moving forward, the success of the flight demonstrations will determine how these AI-enabled EW systems are scaled across the fleet. The use of a Modular Open Systems Approach suggests a goal of interoperability, allowing the Air Force to update algorithms or swap hardware without needing to redesign the entire system. Observers will be watching for the results of the pod demonstrations to see how effectively the AI/ML algorithms perform against real-world emitters.