Adaptive filtering for MEMS gyroscope with dynamic noise model
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Updated: Dec 29, 2025

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
Published on: February 4, 2018
Yuting Bai1, Xiaoyi Wang1, Xuebo Jin1
1School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048, China; China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing, 100048, China.
Abstract:
MEMS (Micro-Electro-Mechanical Systems) gyroscope is the core component in the posture recognition and assistant positioning, of which the complex noise limits its performance. It is essential to filter the noise and obtain the true value of the measurements. Then an adaptive filtering method was proposed. Firstly, noises of MEMS gyroscope were analyzed to build the basic framework of the dynamic noise model. Secondly, the dynamic Allan variance was improved with a novel truncation window based on the entropy features, which referred to the parameters in the noise model. Thirdly, the adaptive Kalman filter was derived from the dynamic noise model. Finally, the simulation and experiment were carried out to verify the method. The results prove that the improved dynamic Allan variance can extract noise feature distinctly, and the filtering precision in the new method is relatively high.
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