Denoising Method of MEMS Gyroscope Based on Interval Empirical Mode Decomposition

Yang Liu, Guangwu Chen, Zongshou Wei, Juhua Yang and Dongfeng Xing

Mathematical Problems in Engineering, 2020, vol. 2020, 1-12

Abstract:

The microelectromechanical system (MEMS) gyroscope has low measurement accuracy and large output noise; the useful signal is often submerged in the noise. A new denoising method of interval empirical mode decomposition (IEMD) is proposed. Firstly, the traditional EMD algorithm is used to decompose the signal into a finite number of intrinsic mode functions (IMFs). Based on the Bhattacharyya distance analysis and the characteristics of the autocorrelation function, a screening mechanism is proposed to divide IMFs into three categories: noise IMFs, mixed IMFs, and signal IMFs. Then, the traditional modelling filtering method is used to filter the mixed IMFs. Finally, the mixed IMFs after modelling and filtering and signal IMFs are reconstructed to obtain the denoised signal. In the experimental analysis, the static denoising experiment of the turntable, the Allan variance analysis, dynamic denoising experiment, and vehicle experiment are set up in this paper, which fully proves that the method has obvious advantages in denoising and greatly improves the quality of signal and the accuracy of the inertial navigation system solution.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3019152

DOI: 10.1155/2020/3019152

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