Date of Award

Spring 2019

Document Type


Degree Name

Master of Science (MS)


Electrical & Computer Engineering

Committee Director

Sachin Shetty

Committee Member

Dimitrie Popescu

Committee Member

Chunsheng Xin


The continuous development of inexpensive embedded sensors has led to the rapid proliferation of new civilian use of unmanned aerial vehicle (UAVs) or drones. It is now easier for civilians to own drones as the cost falls. As we all know drones have a variety of important applications and can also be used for negative effects too. These drones can pose a threat to the security of the population either civilian, organization or industry. There is a need for Radio Frequency Signal Classification (RF-Class) toolbox which can monitor, detect, and classify RF signals from drone communication system. The ability to accurately classify over-the-air radio signals will provide insights into spectrum utilization, device fingerprinting and protocol identification. These insights can help the Warfighter to constantly be informed about adversaries transmitters capabilities without their knowledge. The advantage of the drone detection and classification toolbox is extracting information about transmitters and providing receivers information about transmitted signals. The classification of RF signals will be done based on the modulation scheme, in this case, orthogonal frequency division multiplexing (OFDM). The signal energy and features from the signals leveraging its orthogonal frequency division multiplexing (OFDM) parameter information will be used to classify the signal. This classification will be done using the capabilities of machine learning to train and test the information collected. The content of this thesis discusses how drone detection and classification can be achieved using software defined radio. GNU radio and other hardware components will be used to implement a simulation of the module.


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