Title

Radio Frequency Signal Classification for Drone Detection

Description/Abstract

There is a need to enhance a current Radio Frequency Signal Classification (RF-Class) toolbox that can detect, monitor, and classify wireless signals. This toolbox’s ability to accurately classify over-the-air radio signals will provide insights into spectrum utilization, device fingerprinting and protocol identification. The classification of RF signals is currently done using the modulation scheme, exploitation the cyclostationary features, and leveraging RF band allocation information. Once the modulation scheme is recognized, the scheme is demodulated, decoded, and the packets are extracted. Along with defense, this recognition also has the capability to be used for cyber offensive strategies. This current toolbox has been tested in a high signal-to-noise (SNR) environment. The environment has been stripped of other devices, interferes, and obstacles. Since the created lab environment does not accurately represent the real world, this project has a final goal to enhance the automated RF classification capability in GNU Radio to accurately engineer a system to compete with real-world conditions.

Presenting Author Name/s

Michael Nilsen

Faculty Advisor

Sachin Shetty

Presentation Type

Poster

Disciplines

Signal Processing | Systems and Communications

Session Title

Poster Session

Location

Learning Commons, Atrium

Start Date

2-8-2020 8:00 AM

End Date

2-8-2020 12:30 PM

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Feb 8th, 8:00 AM Feb 8th, 12:30 PM

Radio Frequency Signal Classification for Drone Detection

Learning Commons, Atrium

There is a need to enhance a current Radio Frequency Signal Classification (RF-Class) toolbox that can detect, monitor, and classify wireless signals. This toolbox’s ability to accurately classify over-the-air radio signals will provide insights into spectrum utilization, device fingerprinting and protocol identification. The classification of RF signals is currently done using the modulation scheme, exploitation the cyclostationary features, and leveraging RF band allocation information. Once the modulation scheme is recognized, the scheme is demodulated, decoded, and the packets are extracted. Along with defense, this recognition also has the capability to be used for cyber offensive strategies. This current toolbox has been tested in a high signal-to-noise (SNR) environment. The environment has been stripped of other devices, interferes, and obstacles. Since the created lab environment does not accurately represent the real world, this project has a final goal to enhance the automated RF classification capability in GNU Radio to accurately engineer a system to compete with real-world conditions.