Protecting Autonomous UAVs from GPS Spoofing and Jamming: A Comparative Analysis of Detection and Mitigation Techniques
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".