Modular RFML: Anomaly Detection and Quantification in Electromagnetic Warfare

This webinar explores Integrated Solutions for Systems’ (IS4S), and their partner Virginia Tech National Security Institute (VTNSI), modular approach to Radiofrequency Machine Learning.

New adversarial capabilities, including adaptive and emerging threats, pose a challenge to traditional techniques for signal identification and response. Unlike traditional classification problems used by ML (computer vision, text, audio, etc.), an RF system in a military setting will be tested beyond its training dataset to operational reserve modes, software/cognitive radios, and other adaptive threats.

There is no current magic “silver bullet” to solving this problem, but anomaly detection can help. Anomaly detection and Uncertainty quantification is a field of interest focused on working to gain predictive outcomes when faced with new information in the operating environment. This webinar provides an overview of a modular decision framework of cognitive processes, along with technical approaches, blending operational art and science to identify and detect previously unknown signals in near real-time.

Speaker and Presenter Information

Chris Bieber is a Defense Technology Innovator with expertise in the tactical employment of Tactical Communications, Electronic Warfare, and PNT. Chris founded IS4S Aberdeen in 2020 after retiring from 20+ years in the US Army across infantry, communications, experimentation and systems development. At IS4S, Chris continues to build experience and expertise in the areas of Resilient SATCOM, Artificial Intelligence/Machine Learning, Small Form Factor Electronic Warfare techniques, Software Development, and Assured Positioning, Navigation and Timing (A-PNT).

Dr. William �Chris� Headley (Ph.D. 2015, ECE/Virginia Tech) is the Associate Director for the Spectrum Dominance Division at the Virginia Tech National Security Institute where he has served as a principal or co-principal investigator on a multitude of government and commercial projects totaling over $30M. Within the division, he primarily oversees the Radio Frequency Machine Learning (RFML) and RF Augmented Reality / Virtual Reality (RF AR/VR) portfolios. Through his courtesy appointment within Virginia Tech�s Electrical and Computer Engineering department, he also serves as a mentor and advisor to both undergraduate and graduate student researchers, providing them with hands-on research opportunities through these projects as well as guiding them towards their degree requirements. In particular, he has overseen multiple educational and workforce development focused grants in the past including multiple Naval Engineering Education Consortium grants (with both Naval Surface Warfare Center Crane and Dahlgren), with the Intelligence Community Centers for Academic Excellence (ICCAE) , and with multiple businesses (Amazon Web Services and CACI) . He has written over 50 conference/journal publications and holds an active TS-SCI clearance.