A NAVIGATION METHOD FOR AUTONOMOUS UNMANNED VEHICLES UNDER INTERFERENCE CONDITIONS

Abstract

Autonomous unmanned aerial vehicles require reliable navigation when satellite measurements are degraded, blocked, or intentionally falsified. The relevance of this research is determined by the growing use of UAVs in autonomous missions where GNSS signals may be affected by jamming, spoofing, multipath propagation, or limited satellite visibility. The objective of the paper is to develop a navigation method that preserves a physically plausible UAV trajectory under interference conditions. The proposed approach combines IMU/INS prediction, controlled GNSS correction, adaptive Kalman filtering, and semantic map matching. GNSS measurements are used only when they are consistent with the predicted inertial state. In the case of jamming, the estimator temporarily relies on INS propagation. In the case of spoofing, suspicious measurements are rejected according to abnormal innovation values. To reduce long-term drift, semantic information from UAV camera data is compared with reference map layers, including roads, shorelines, and buildings. Simulation results show that pure inertial navigation accumulates significant positioning error, whereas the adaptive fused estimate remains bounded during jamming and spoofing intervals. The proposed method improves navigation resilience by combining radio navigation, inertial estimation, and visual map constraints. Further research should focus on real flight validation and real-time implementation.