An Innovative Anti-Spoofing Algorithm with an Onboard Real-Time Simulation Model of the Aircraft

When a UAV is subjected to GPS spoofing, the attacker manipulates key navigation parameters, such as azimuth (heading) and Earth-centered, Earth-fixed (ECEF) coordinates (X, Y, and Z). This leads to inaccurate positioning, causing the UAV to deviate from its intended flight path. A dual-frame simulation architecture is developed, combining a high-fidelity reference model with a real-time flight simulation model running on embedded hardware to evaluate anti-spoofing robustness. Aerodynamic sensitivity is quantified using a Monte Carlo analysis, which identifies pitch moment, yaw moment, and lateral/vertical force lookup tables as the dominant contributors to estimator divergence. A modular aerodynamic coefficient pipeline is introduced, separating datum coefficients, dynamic damping derivatives, and perturbation layers for controlled uncertainty injection. A two layer perturbation framework is implemented to distinguish natural aerodynamic mismatch from spoofing induced inconsistencies during simulation. Cross links between the real AHRS and the onboard flight sim model provide mutual consistency checks, significantly reducing the impact of malicious sensor manipulation. The proposed architecture enables dynamic lookup table switching and morphing during simulation, supporting realistic degradation, drift, and spoofing scenarios. Results demonstrate that physics based cross validation between models and sensors enhances spoofing detection while maintaining robustness to aerodynamic uncertainty.