Electrical and Computer Engineering ETDs

Publication Date

Fall 11-14-2023


The contemporary GPS infrastructure plays a vital role in advancing Position, Navigation, and Timing (PNT) applications across various sectors, including transportation, commerce, finance, and aviation. To provide a robust and authenticated infrastructure for users, it is important to develop a methodology for verifying the authenticity of satellite navigation signals, especially in civilian applications using the C/A code. This thesis explores Machine Learning (ML) techniques, focusing on the analysis of Global Navigation Satellite System (GNSS) protocol messages like UBX and NMEA to detect anomalies in receiver and satellite behavior, indicative of potential spoofing. The research demonstrates the deployment of Machine Learning models on a specialized Xilinx Kria KV260 FPGA hardware platform, enabling real-time solutions for signal authentication that can be readily deployed in the field, effectively addressing the need for signal security and validation.


GPS Spoofing Detection, Machine Learning in PNT, GNSS Protocol Analysis, Signal Authentication, Machine Learning Models Deployment in Hardware Xilinx Kria KV260 FPGA, Real-Time Anomaly Detection

Document Type




Degree Name

Computer Engineering

Level of Degree


Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Dr. Eirini Eleni Tsiropoulou

Second Committee Member

Dr. James Aarestad

Third Committee Member

Mr. Brian Zufelt