The UAV Altimetry Method Based on Extended Fault-Tolerant Kalman Filter

Multi-rotor drones are celebrated for their flexibility and efficiency, and are extensively employed in agriculture, mapping, and leisure activities. Nonetheless, the altitude sensors of these drones can sometimes be disturbed by severe noise, and their lack of sensitivity to altitude changes, coupled with sensor damage, can lead to erroneous judgments and consequent crashes. Addressing the issues of sensor failure and the sensitivity of traditional Kalman filtering and multi-sensor data fusion, a UAV altimetry method based on extended fault-tolerant Kalman filtering has been proposed. This method aims to tackle sensor malfunctions and the insufficient sensitivity of traditional Kalman filtering techniques on a software level. The specific strategy involves implementing a Chi-square test following the traditional Kalman filter to address sensor failures, incorporating a second-order exponential smoothing (EMA) algorithm to enhance the filter's sensitivity, and selecting appropriate functional formulas for handling sensor failures.