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Research on Filtering Algorithm of MEMS Gyroscope Based on Information Fusion

College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
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Sensors 2019, 19(16), 3552; https://doi.org/10.3390/s19163552
Received: 2 July 2019 / Revised: 7 August 2019 / Accepted: 13 August 2019 / Published: 15 August 2019
(This article belongs to the Special Issue Gyroscopes and Accelerometers)
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Abstract

As an important inertial sensor, the gyroscope is mainly used to measure angular velocity in inertial space. However, due to the influence of semiconductor thermal noise and electromagnetic interference, the output of the gyroscope has a certain random noise and drift, which affects the accuracy of the detected angular velocity signal, thus interfering with the accuracy of the stability of the whole system. In order to reduce the noise and compensate for the drift of the MEMS (Micro Electromechanical System) gyroscope during usage, this paper proposes a Kalman filtering method based on information fusion, which uses the MEMS gyroscope and line accelerometer signals to implement the filtering function under the Kalman algorithm. The experimental results show that compared with the commonly used filtering methods, this method allows significant reduction of the noise of the gyroscope signal and accurate estimation of the drift of the gyroscope signal, and thus improves the control performance of the system and the stability accuracy. View Full-Text
Keywords: MEMS gyroscope; line accelerometer; noise; drift; Kalman filter MEMS gyroscope; line accelerometer; noise; drift; Kalman filter
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Guo, H.; Hong, H. Research on Filtering Algorithm of MEMS Gyroscope Based on Information Fusion. Sensors 2019, 19, 3552.

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