Trend extraction of the MEMS Gyroscopes drift Based on EEMD
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, gyros 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 Gyroscopes drift. The non-linear and nonstationary 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 Gyroscopes drift signals. The experiment result indicates that the method can effectively extract the trend of the drift.
drifl ensemble empirical mode decomposition MEMS gyroscope time series trend extraction
Zhang Yinqiang Wang Shourong Xia dunzhu
Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education Southeast University Nanjing, China
国际会议
长沙
英文
3304-3307
2010-03-13（万方平台首次上网日期，不代表论文的发表时间）