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Micromachines 2015, 6(2), 266-280;

Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal

Xi'an Research Inst. of Hi-Tech, Hongqing Town, Xi'an 710025, China
MOE Key Laboratory of Micro and Nano Systems for Aerospace, Northwestern Polytechnical University, 127 Youyi West Road, Xi'an 710072, China
Author to whom correspondence should be addressed.
Academic Editors: Naser El-Sheimy and Aboelmagd Noureldin
Received: 24 December 2014 / Revised: 7 February 2015 / Accepted: 10 February 2015 / Published: 16 February 2015
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
View Full-Text   |   Download PDF [483 KB, uploaded 16 February 2015]   |  


In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by a steady-state filter gain obtained from the analysis of KF observability. In particular, the true angular rate signal was directly modeled to obtain an optimal estimate and make a self-compensation for the gyroscope without needing other sensor’s information, whether in static or dynamic condition. A linear fit equation that describes the relationship between the KF bandwidth and modeling parameter of true angular rate was derived from the analysis of KF frequency response. The test results indicated that the MEMS gyroscope having an ARW noise of 4.87°/h0.5 and a bias instability of 44.41°/h were reduced to 0.4°/h0.5 and 4.13°/h by the KF under a given bandwidth (10 Hz), respectively. The 1σ estimated error was reduced from 1.9°/s to 0.14°/s and 1.7°/s to 0.5°/s in the constant rate test and swing rate test, respectively. It also showed that the filtered angular rate signal could well reflect the dynamic characteristic of the input rate signal in dynamic conditions. The presented algorithm is proved to be effective at improving the measurement precision of the MEMS gyroscope. View Full-Text
Keywords: MEMS gyroscope; noise reduction; Kalman filter; direct modeling; random walk process MEMS gyroscope; noise reduction; Kalman filter; direct modeling; random walk process

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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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Xue, L.; Jiang, C.; Wang, L.; Liu, J.; Yuan, W. Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal. Micromachines 2015, 6, 266-280.

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