Research of random drift for MEMS gyroscope based on gray system and ARMA model

Due to the precision of MEMS gyroscope is currently low level, even in low precision dynamic attitude measurement system, its error is also need to be estimated and compensated. Random drift is the one of the most important factor affects MEMS Gyro's precision. At the same time, it is a non-stationary, weak non-linear and time-variant random signal. For improving precision of gyro and reducing effects of random drift, this paper used gray GM(1, 1) model to extract established tendency signal, and used power spectral density (PSD) to identify and extract the hidden periodic weak signal, then used ARMA method to model gyro's random drift. As the example of LCG50 MEMS gyro, the simulation experimental results show that using this method can reduce the random drift and enhance precision of gyro.