Adaptive Barometer/INS Fusion for UAV Altitude Estimation Under Strong Wind Disturbances

In GPS-denied environments, unmanned aerial vehicles (UAVs) commonly fuse barometric measurements with inertial navigation system (INS) data to estimate altitude and vertical velocity. However, barometric accuracy degrades significantly under strong wind disturbances. To address this issue, this paper proposes a wind-adaptive fusion Kalman filter (WAF-KF). First, an event-triggered mechanism based on a wind-scale potential energy function is designed to adaptively adjust barometric sampling intervals and trust weights, thereby mitigating wind-induced interference. Second, the event-triggered mechanism is integrated with Huber M-estimation to enhance a colored-noise Kalman filter (KF), improving both fusion efficiency and estimation accuracy. Experimental results demonstrate that the proposed method reduces the root mean square error (RMSE) of altitude and vertical velocity estimation by up to 68.1% and 86.5%, respectively, compared with standalone barometers, extended Kalman filters (EKF), and cubature Kalman filters (CKF). These results indicate that the proposed approach provides a robust solution for UAV navigation in complex environments.