A Novel Filtering Method for the Random Drift of MEMS Gyroscope

Abstract:

In engineering application, the nonlinearity effect of the environment noise is inconsistent with the successive starting state of MEMS gyroscope which will induce the random drifts. It manifests as the weak nonlinearity, non stability and slow time varying which cannot be compensated by the conventional method. In order to overcome the problems of the great random drift error model established based on the time series for MEMS gyroscope and the non Gaussian noise, the method of Iteration Unscented Kalman Particle Filter (IUKPF) is proposed in this paper. This method is based on the Particle Filter combing the Unscented Transformation (UT) with Iteration Kalman Filter (IKF), and it solved the instability of the precision for the conventional filtering methods and the degradation for the weight of the particle filter. The filtering result shows that the method of IUKPF can effectively restrain the random drift error under nonlinear and non Gaussian noise. The standard deviation for the output noise of MEMS gyroscope has decreased 81.9% by IUKPF which verifies the efficiency and superiority of this method.

• Periodical: Advanced Materials Research (Volume 901)
• Pages: 73-79
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.901.73
• Online since: February 2014
• Authors: Yu Wang, Yun Xu, Xin Hua Zhu*
• Keywords: MEMS Gyroscope, Particle Filter (PF), Random Drift, Unscented Transformation

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