Research on Filtering Algorithm of MEMS Gyroscope Based on Information Fusion

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 16; p. 3552
• Main Authors: Guo, Hui, Hong, Huajie
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 15.08.2019; MDPI
• Subjects: Accelerometers; Accuracy; Algorithms; drift; Error analysis; Fiber optics; Kalman filter; line accelerometer; MEMS gyroscope; Methods; Microelectromechanical systems; Neural networks; Noise; Sensors; Simulation; Standard deviation; Time series; Velocity; China; MEMS gyroscope; noise; line accelerometer; Kalman filter; drift
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19163552

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.