A METHOD FOR DECISION-MAKING IN DRONE NAVIGATION UNDER SENSOR JAMMING CONDITIONS BASED ON KALMAN FILTER

Abstract

In environments where GPS, remote radio control, or radio communication are unreliable or subject to jamming, small drones must navigate using limited onboard autonomous sensors and detectors. This article investigates the stability of a lightweight autopilot for a drone in combination with a Kalman filter and gating for each sensor under simulated jamming scenarios. Jamming is modeled as Poisson pulses, dropouts, additive offset, and increased signal dispersion in four onboard sensors: IMU, magnetic compass, LiDAR, and camera-based optical flow.

Using 20 planned experiments, the effectiveness of navigation (RMSE of position in space and speed, mission duration, energy consumption) and sensor behavior during decision-making (share of rejected measurements) were measured. The results show that under conditions of moderate jamming, a simple Kalman filter architecture provides opportunities for effective navigation along route points, but strong, prolonged, and nonlinear jamming of sensors leads to deterioration in accuracy or navigation failure. This suggests that even computationally simple decision-making using Mahalanobis distance filtering can provide reliable navigation in certain scenarios where GNSS connectivity is unavailable.