Research on Information Fusion Technology of MEMS Gyro Array. Issue 4 (June 2021)

• Record Type: Journal Article
• Authors: Xue, Chao; Zhang, Yinqiang; Li, Lijuan; Yang, Shipin
• Is Part Of: Journal of physics. Volume 1952:Issue 4(2021)
• Publication Date: 2021-06
• Subjects: MEMS gyroscope array -- time series analysis -- ensemble empirical mode decomposition -- the moving horizon optimization -- Allan variance -- Kalman filter; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1952/4/042015
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The measurement accuracy of MEMS gyroscope is relatively low. Starting from the software level, the multi-sensor information fusion technology of MEMS gyroscope array (MGA) is used to reduce the drift of MEMS gyroscope. Firstly, a signal acquisition and processing system for communicating with ADXRS810 gyroscope based on field-programmable gate array (FPGA) is designed. Secondly, the collected drift data of the gyroscope array is preprocessed. long-term drift trend terms are obtained by ensemble empirical mode decomposition (EEMD) and the linear function fitting, and the trend terms are filtered out to obtain a smooth and normal drift signal. Then, the data fusion model based on time series analysis is built for the gyroscope array and the error analysis is performed by Allan variance. Finally, based on the AR (1) model, the moving horizon optimization Kalman filter method is used to filter the drift data of the gyroscope array. The experiment shows that when the time domain length N=3, the bias instability of gyroscope 1, 2, 3 and 4 decreases from 9.716 o/h, 8.5682 o/h, 13.484 o/h and 26.414 o/h to 1.2922 o/h, 0.61147 o/h, 1.4184 o/h and 1.6964 o/h, respectively, and the average noise coefficient decreases by more than 85%. Compared with the ordinary Kalman filter method, the bias instability has been significantly improved.