Protecting Autonomous UAVs from GPS Spoofing and Jamming: A Comparative Analysis of Detection and Mitigation Techniques

Abstract

This study investigates the vulnerabilities of unmanned aerial vehicles (UAVs) to GPS spoofing and jamming, addressing three key research questions: (1) What are the common techniques used to spoof or jam GPS signals for UAVs? (2) How do these techniques impact UAV performance and safety? (3) What mitigation strategies are most effective in preventing interference? A mixed-methods approach was used, combining a qualitative review of peer-reviewed literature and a quantitative analysis of GPS signal data. Spoofing increased positioning errors to 20.45 meters, while jamming reduced mission completion rates by 40%. Detection models, including Random Forest, SVM, and Neural Networks, were evaluated, with SVM showing a recall of 56.4% for spoofed signals despite lower overall accuracy. Inertial Navigation Systems (INS) and Visual Odometry were most effective in reducing navigation errors by over 90% and showed the highest mission success rates, recovering from interference within 0.81 to 1.28 seconds. These findings highlight the importance of integrating advanced detection methods and resilient systems in GPS-reliant UAV operations.