Attitude Algorithm Based on MACF-CKF Multi-Sensor Fusion
Targeting at the problem that the unit attitude solving accuracy is low for the inertial navigation measurement,an attitude calcu-lation algorithm based on the fusion of multi-sensor membership adaptive complementary filtering(MACF)and volumetric Kalman filter(CKF)is proposed.The exponential weighted moving average is used to correct the gyro noise deviation.In order to avoid the large error of pitch angle and roll angle caused by the improper distribution of the weight of gyroscope and accelerometer in the complementary filtering,the membership function of gyro deviation is constructed to judge the trust of complementary filtering to gyroscope,and the adaptive factor of complementary filtering is dynamically adjusted according to the trust,and the problem of course angle divergence is solved by CKF with magnetometer and gyroscope.Experimental results show that the proposed algorithm can achieve the attitude solution quickly and accurate-ly under both static and dynamic conditions.In the dynamic vehicle experiment,the accuracy of roll angle and pitch angle increases by 24.5%and 63.2%respectively,and the heading angle increases by 48.8%,which can guarantee the solution accuracy.
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