AI-powered real-time RF spectrum signal classifier
This technology uses a convolutional neural network to automatically scan the radio frequency spectrum, identify what types of signals are present, and pinpoint where they are. It works by chopping up a continuous stream of radio data into segments and comparing each segment against a library of known signal types using a distance-based matching approach — essentially asking 'how far is this chunk of radio data from signals we already know?' The system can handle signals it hasn't seen before by flagging them as unrecognized rather than forcing a wrong match. The result is a software system that continuously monitors airwaves and returns labeled, classified signal events in near real time.
What you could build
A software platform for real-time RF spectrum monitoring and signal classification, sold to defense contractors, cellular network operators, and spectrum regulators who need to detect interference, unauthorized transmitters, or electronic warfare activity without human analysts reviewing raw spectrum data.
Who in Virginia should care
Northern Virginia and the Hampton Roads corridor host dense concentrations of defense primes, signals intelligence contractors, and DoD agencies with active spectrum management and EW procurement budgets.
Readiness: Prototype likely
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, Tamoghna Roy
- Granted
- April 18, 2023
- Status
- Granted patent
- Patent number
- 11630996
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.