Multi-sensor data fusion for indoor drone positioning

The positioning of indoor mobile machines cannot be solved by a single sensor. A positioning system based on multi-sensor data fusion is designed. The system combines the positioning results of UWB with the positioning results of low-cost MEMS inertial measurement elements to improve the positioning accuracy of the system. The system includes two Kalman filters. The primary Kalman filter is used to fuse the angular velocity information of gyroscope and the angular value of magnetometer, so as to obtain more accurate heading angle. The secondary Kalman filter combines the IMU track estimation results with the UWB positioning results to obtain accurate positioning results. The final experimental results show that the method is effective.