In order to effectively suppress the random errors of micromechanical gyroscopes,an improved noise reduction method for mi-cromechanical gyroscopes is proposed based on improved empirical mode decomposition( MEEMD) combined with particle swarm opti-mization algorithm( QPSO) optimized Kalman filtering( KF) . The micromechanical gyro data are decomposed by MEEMD to obtain the eigenmode components,which are classified by using the multiscale entropy algorithm,and the QPSO-KF algorithm is applied to the sig-nal noise mixed components among them. Then,the filtering results and the signal-dominated components are reconstructed to realize the micromechanical gyro signal noise reduction. The experiments verify the effectiveness of the method,and compared with the traditional empirical modal decomposition( EMD) ,KF accuracy is improved by 1 orders of magnitude,verifying the effectiveness and accuracy of the proposed method.
万方数据
维普
