An attitude estimate approach using MEMS sensors for small UAVs

For the small UAVs (Unmanned Aerial Vehicle) using MEMS sensors, this article puts forward a Kalman Filter model to get attitude estimate without long term drift and showing relatively smaller error. Firstly, strapdown inertial attitude algorithm and bi-vector attitude algorithm are presented, which are widely used in small UAV autopilot systems now. However, there is a problem of long term drift with the former and heavy noise with the latter. Due to these shortcomings, accurate attitude control has not been achieved yet in small UAVs. In order to solve these problems, this paper gives out a Kalman filter model which fuses the two types of data Into an optimal estimate of real attitude, and overcomes the shortages of both algorithms mentioned above. Simulation results show that this filter can be used to gain fairly good data for more accurate attitude control. Besides, compared with the filters already developed, this Kaiman filter has a relatively low order and a loose architecture, which could be more easily adopted in an existed embedded computer system of small UAV.