Trend Extraction of the MEMS Gyroscope's Drift Based on EEMD

Abstract

The Micro Electro Mechanical System (MEMS) gyroscopes are widely used in many applications for the compact size and low cost. However, since the MEMS gyroscopes usually have large drift which affects the measuring precision, we need to extract the trend of drift signal and eliminate the determinate trend to reduce the disadvantageous affection. Generally, gyro's drifts are a weak nonlinear and non-stationary random process. Thus, non-stationary time series analysis is needed. In this paper, we propose a novel approach based on the ensemble empirical mode decomposition (EEMD) to extract the trend item of the MEMS Gyroscope's drift. The non-linear and non-stationary drift signals are decomposed into a series of intrinsic mode functions and a residual trend item by the EEMD. The method overcomes the shortcomings of the mode mixing and represents an improvement of the EMD method. The concrete steps of the proposed approach are presented and applied to a MEMS Gyroscope's drift signals. The experiment result indicates that the method can effectively extract the trend of the drift.