Abstract:

In order to reduce the random error in the measurement data of a micro-electromechanical system(MEMS) gyroscope,an improved complete ensemble empirical mode decomposition with the adaptive noise-forward linear predictive filtering (CEEMDAN-FLP) hybrid noise reduction method is proposed when the sudden change of the carrier motion state causes the step change of gyro sensor data.The improved algorithm uses a soft interval thresholding filter for low-order noise intrinsic mode functions (IMFs),which avoids the problem of high frequency signal loss caused by the conventional method of removing noisy IMFs directly.At the same time,an FLP filter is used for the mixed IMFS to avoid excessive filtering caused by threshold elevation.Finally,data reconstruction is carried out between the filtering results and signal IMFs.Simulation results show that the root-mean-square error of the improved algorithm is reduced by 51.53% compared with the original signal,and by 17.39% compared with the EMD filtering algorithm.The measured data verify that the gyro data filtered by the improved algorithm and the gyro data filtered by the CEEMDAN algorithm are respectively used for attitude calculation,and that the attitude cumulative error of the improved algorithm is only 20.56% of the attitude cumulative error of the conventional algorithm without significantly increasing the operation burden.It can be seen that the improved algorithm can effectively improve the measurement accuracy of the sensor.

Key words: empirical mode decomposition, complete ensemble empirical mode decomposition with adaptive noise, intrinsic mode functions, forward linear prediction algorithm