基于多传感器融合的EKF无人机状态估计

When UAVs perform missions in complex environments, reliable state estimation is crucial for efficient autonomous flight. Traditional single-sensor approaches are inadequate in addressing issues such as signal loss, measurement drift, and environmental interference, which has led to multi-sensor information fusion becoming a major research focus. To this end, this paper proposes a multi-sensor fusion state estimation method based on the Extended Kalman Filter (EKF). By constructing a UAV dynamic model and establishing a unified filtering framework, the method effectively fuses heterogeneous sensor data from accelerometers, gyroscopes, magnetometers, GPS, and barometers, thereby accurately estimating the UAV\'s position, velocity, and attitude. Simulation results indicate that, compared with single-sensor schemes, the proposed algorithm effectively suppresses sensor noise and integration drift while demonstrating significant advantages in dynamic response performance and measurement accuracy. This technology provides solid technical support for UAV missions in more complex and variable environments and lays a foundation for its broader application.