Research on automatic early warning of UAV attitude abnormal state based on MEMS sensor

Abstract

The Unmanned Aerial Vehicle's (UAV) attitude control is crucial to the success of the mission. On the basis of this, the paper suggests a paradigm for autonomous early warning of improper UAV attitude based on MEMS sensors. To obtain early warning of anomalous UAV attitude, the model solves UAV attitude using the quaternion approach and employs a fading Kalman filter to correct for MEMS gyroscope inaccuracy. The simulation test demonstrates that in the static state, the errors of the drone's pitch angle and roll angle are within 0.2°, and the heading angle error is about 0.5°. In the high manoeuvring state, the errors of the UAV's pitch angle and roll angle are all within 0.5°, and the mean value of the heading angle error is also controlled within 2°. The experiment achieves high-precision automated warning of aberrant attitude by filtering the fading Kalman filter to correct the random error of the UAV gyroscope. It also increases the precision with which human motion is measured. The suggested approach promotes the growth of the UAV sector.