Fusion Location Method of Multi-Rate Adaptive Kalman Filtering

Aiming at the problem that the location is inaccurate with single sensor and the output frequency of the location data from different sensors is inconsistent, the accuracy and real-time of UAV location is improved by using the redundant information of acceleration and displacement sensor as much as possible. This paper presents an integrated location method with Multi-Rate Adaptive Kalman Filter (MRAKF) of seamless fusion. Firstly, the error model of inertial navigation system and GPS location are established to analyze position error; Secondly, during the measurement gap of the GPS data output, the acceleration feedforward seamless fusion method is used to obtain the estimate of location variety, and then the system give the location in real time. Finally, the information of laser, barometer, GPS and inertial navigation system is effectively fused to obtain accurate location estimation with the MRAKF method, and acceleration modify location estimation in the maximum measurement period of each sensor at the same time. Experimental results show that this presented method can improve the accuracy of position and the error is less than 0.1m while ensuring the location of real-time.