基于EKF算法的太阳能无人机低成本飞控状态估计

When the flight controller composed of low-cost sensors is applied to a large aspect ratio solar-powered unmanned aerial vehicle (UAV), it is limited by the accuracy of the sensors, the long-endurance, and wide-range task requirements. The traditional data fusion algorithm cannot realize its accurate and reliable estimation of attitude, airspeed and wind field for a long time. Starting from the sensor measurement principle of the flight controller, the error characteristics and temperature effects of the measurement process are modelled, and a reliable state estimation is realized based on the extended Kalman filter algorithm. Firstly, the pressure sensor and inertial measurement unit (IMU) data are combined to achieve attitude estimation. Then, combined with the layout characteristics of the UAV, the magnetometer is independently installed and the heading is estimated. Finally, GPS data is merged for navigation estimation. The simulation results show that compared with the variable gain observe algorithm, the proposed algorithm is more hierarchical and the estimation results are more reliable, and it can be combined with the characteristics of the solar-powered UAV.