Transformer-based AI module for radio emitter identification
This technology uses a two-stage AI system to better understand radio signals being received over a wireless channel. A first neural network analyzes the signal and produces preliminary information, which is then fed—along with the original signal—into a 'signal transformer' that reshapes and conditions the signal based on how the channel is behaving. A second neural network then processes this transformed signal to extract detailed information about where the signal came from and who is transmitting it. The result is a smarter radio receiver that can identify emitters and infer context that traditional signal processing would miss.
What you could build
An AI-powered signal intelligence module embedded in spectrum monitoring systems, software-defined radios, or cellular base stations that can identify and classify radio emitters in real time; primary buyers would be defense contractors, spectrum regulators, and telecom equipment vendors.
Who in Virginia should care
Northern Virginia and the Hampton Roads corridor host major defense primes (Leidos, SAIC, Booz Allen) and Navy/DoD spectrum operations that actively seek AI-enhanced signal intelligence capabilities.
Readiness: Lab validated
Concept — described but not yet demonstrated. Lab validated — supported by experimental results in the patent. Prototype likely — the text describes a built, working embodiment.
Readiness is inferred from the patent text, not from a lab visit.
The record
- Inventors
- Timothy James O`Shea
- Granted
- August 13, 2024
- Status
- Granted patent
- Patent number
- 12061982
Ready to talk?
Virginia Tech Intellectual Properties handles licensing for this technology.
Prosim summaries are generated from public patent text and are not legal advice.