Development of Steady Aiming System Based on Kalman Filter and Coordinate Transformation

The aiming accuracy of the Unmanned Aerial Vehicles (UAV) Steadicam head can be affected by many factors, such as the state of the UAV during the actual flight and the installation error of the system related hardware. In order to eliminate the influence of objective factors on the UAV Steadicam, a Kalman filter aiming algorithm based on the coordinate transformation method is proposed to eliminate the attitude error of the UAV Steadicam and improve the accuracy of the system. The algorithm uses coordinate transformation to eliminate mounting errors and combines coordinate transformation and Kalman filtering methods to eliminate objective errors of the UAV in flight. The experimental simulation results show that our method can accurately give the amount of azimuth and pitch angle error compensation during the flight of the UAV, improving the accuracy of the UAV Steadicam head. Ultimately, the method is applied to the development of a real product.