Protecting Autonomous UAVs from GPS Spoofing and Jamming: A Comparative Analysis of Detection and Mitigation Techniques
Princess Chimmy Joeaneke
*
University of the Cumberlands, 104 Maple Drive, Williamsburg, KY 40769, United States of America.
Onyinye Obioha Val
Department of Computer and Electrical Engineering, University of District of Columbia, 4200 Connecticut Avenue NW, Washington DC 20008, United States.
Oluwaseun Oladeji Olaniyi
University of the Cumberlands, 104 Maple Drive, Williamsburg, KY 40769, United States of America.
Olumide Samuel Ogungbemi
Centennial College, 941 Progress Ave, Scarborough, ON M1G 3T8, Canada.
Anthony Obulor Olisa
Cumberland University, 1 Cumberland Dr, TN 37087, Lebanon.
Oluwaseun Ibrahim Akinola
Olabisi Onabanjo University, P.M.B 2002, Ago-Iwoye, Ogun State, Nigeria.
*Author to whom correspondence should be addressed.
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.
Keywords: GPS spoofing, UAV interference, mixed-method analysis, multi-sensor fusion, anti-jamming strategies