超级航姿中基于变阈值判据的自适应Kalman滤波

Aiming at the disadvantage that the traditional AHRS algorithm may cause the carrier maneuver state misjudgment and leads to the filter oscillation or even divergence when the output stability of the accelerometer in the super-AHRS is changed due to the interference of environment, an adaptive Kalman filter algorithm based on the variable-threshold carrier maneuvering criterion based on fuzzy inference system (FIS) is presented in this paper. The algorithm can adaptively adjust the carrier maneuver criterion and the measurement noise array of the filter according to the change of the output stability of the accelerometer, thereby reducing the misjudgment rate of the carrier maneuvering state and improving the utilization of Kalman filter for measurement information. The simulation experimental results show that the algorithm can still judge the maneuver state of the carrier well and make the corresponding adjustment to the filter when the output stability of the accelerometer is changed, which improves the filter stability and the long-range attitude accuracy of the super-AHRS.