Computer Modeling and Parametric Analysis of GNSS Receiver Jamming Resilience for Aerial Vehicles
DOI:
https://doi.org/10.32515/2664-262X.2026.13(44).83-89Keywords:
digital signal processing, adaptive filtering algorithm, anti-jamming factor, aerial object, electromagnetic interference, electronic warfare, controlled reception pattern antennaAbstract
The article addresses the problem of developing mathematical models and software for computer simulation of electromagnetic interference effects on navigation subsystems of cyber-physical systems, specifically unmanned aerial vehicles that rely heavily on signals from global navigation satellite systems for positioning and navigation, which creates a critical vulnerability to intentional electromagnetic interference. Existing engineering models often fail to account for modern anti-jamming techniques and do not consider the uncertainty of real-world parameters. This creates a need for computational models capable of stochastic analysis and confidence interval estimation.
The proposed approach is based on modeling radio channel energy parameters using the Friis equation extended with a generalized anti-jamming factor that characterizes digital signal processing algorithm efficiency. To account for parameter uncertainty, the Monte Carlo method with ten thousand simulations has been implemented in Python programming language. The simulation generates random parameter variations following lognormal distribution and computes suppression range values. Complete source code is provided, enabling full reproducibility of results. Model sensitivity analysis using tornado diagrams identifies the most critical parameters.
Verification was conducted through comparison with experimental data from published literature. For the Spanghero experiment with one watt jammer, the model predicts 2.1 kilometers against experimental 1.5 to 3 kilometers. For the Rozenbeek data with anti-jamming factor of ten, the model yields 7 kilometers compared to experimental 5 to 10 kilometers. Relative error does not exceed thirty percent. Results demonstrate that increasing the anti-jamming factor from unity to one thousand reduces suppression range from 22.34 to 0.71 kilometers. The practical value lies in applicability for designing protection algorithms for cyber-physical navigation systems.
References
Список літератури
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References
1. Spanghero, F. Geib, R. Panier and P. Papadimitratos, "Uncovering GNSS Interference with Aerial Mapping UAV," 2024 IEEE Aerospace Conference, Big Sky, MT, USA, 2024, pp. 1-10, DOI: 10.1109/AERO58975.2024.10521434.
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3. Yang, Xin & Liu, Wenxiang & Chen, Feiqiang & Lu, Zukun & Wang, Feixue. (2019). Analysis of the Effects Power-Inversion (PI) Adaptive Algorithm Have on GNSS Received Pseudorange Measurement. IEEE Access. PP. 1-1. DOI: 10.1109/ACCESS.2019.2952886.
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Copyright (c) 2026 Pavlo Novitskyi, Mykhailo Stepanyak
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