Abstract—The accuracy of MEMS inertial sensors is affected by random errors. Kalman Filter is the commonly used approach in reducing the random errors of MEMS sensor output. However, this approach is restricted by some dissatisfaction e.g. the divergent problem and the fixed noise covariance matrix Q and R’s inability to represent the dynamic noise characteristics of the system. In this paper, Particle filtering method is employed to reduce the random errors of MEMS gyro output. By a set of samples, Particle Filter is able to represent the posterior distribution of the states in a dynamic system when partial observations are made and random perturbations are present in sensor outputs. Experiments with artificial data sequence as well as authentic ADIS16445 inertial gyro output are conducted and then Particle Filter and Kalman Filter method are introduced to process these signals. Allan Variance analysis reveals that two main random errors are notably diminished after both filtering method and noticeably Particle Filter is superior to Kalman Filter in reducing MEMS gyro random noise.
Index Terms—MEMS gyro, random error, Particle Filter, Kalman Filter.
Yingjie Hu is with the School of Automotive Studies, Tongji University, Shanghai 201804 China (e-mail: redheart9527@ 163.com).
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Cite: Yingjie Hu, "A New Approach in Reducing MEMS Inertial Gyro Random Errors Using Particle Filter," International Journal of Materials, Mechanics and Manufacturing vol. 7, no. 4, pp. 175-179, 2019.
Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (
CC BY 4.0).
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