Abstract
Aiming at the problem that the signal of microelectromechanical system (MEMS) gyroscope is disturbed by random noise to reduce the measurement accuracy, combined with the chi-square detection theory and the classification method of signal correlation degree, an improved empirical mode decomposition (EMD) noise reduction method of MEMS gyroscope based on Chi-square detection is proposed. Firstly, the method constructs chi square variables, removes the measured outliers that cannot be detected by EMD decomposition and have a great impact on the envelope fitting of EMD algorithm based on Chi square detection, then EMD decomposes the signals after outliers elimination, defines the joint correlation of the intrinsic mode functions (IMF) components according to Pearson correlation coefficient, performs non-linear screening, and KF denoises the IMF components with mixed effective signal and noise, IMF components are classified and reconstructed based on joint correlation. The semi physical simulation results show that compared with the EMD noise reduction method classified according to the correlation coefficient, this method improves the signal-to-noise ratio, effectively reduces the wild noise and random noise, and improves the measurement accuracy of MEMS gyroscope.
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Ming, L., Jinhui, L., Jiayun, Z., Yifan, Y., Yanfeng, W. (2023). EMD Noise Reduction Method of MEMS Gyroscope Based on Chi-Square Detection. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_16
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DOI: https://doi.org/10.1007/978-981-19-6613-2_16
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