Spectral Awareness and Interference Rejection


This project seeks to apply AI to enhance the USAF’s ability to detect, identify, and geolocate unknown radiofrequency (RF) signals, while providing tools for adaptive interference mitigation and smart spectrum analysis. These capabilities enhance Air Force Intelligence Surveillance and Reconnaissance (ISR) missions, communications, signals intelligence (SIGINT), and electronic warfare. Results will increase bandwidth utilization efficiency and spectrum sharing, improve Air Force communications performance in high interference environments, produce higher-quality RF signals intelligence, and improve system robustness to adversarial attacks and interference.

Team Wornell
– Gregory W. Wornell (MIT PI)
– Yury Polyanskiy (MIT Co-PI)
– Alexia Schulz (MIT Lincoln Laboratory Lead)
– Keith Forsythe (MIT Lincoln Laboratory Lead)
– Jovan Popovich (DAF Liaison)


Published Research

To learn more about Spectral Awareness and Interference Rejection research and other AI Accelerator projects, view our published research here.

Are you up for a Challenge?

The Radiofrequency Challenge aims to encourage the development of new AI inspired algorithms and stimulate research incorporating contemporary notions and novel ML techniques, leading to enhanced spectral awareness and interference rejection capabilities. Learn more about the challenge here.