An LMD-LMS-based Denoising Method for MEMS Gyroscopes
CHANG Honglei
LI Jie
HU Chenjun
SUN Pengxiang
WANG Jingqi
Xia Junhui
Abstract：MEMS(Micro-Electro-Mechanical-System)gyroscopes are miniaturized inertial sensors that are widely used in navigation,missile guidance,autonomous driving,virtual reality,and UAVs.However,MEMS gyroscopes are often affected by noise from the environment and the hardware itself,which reduces their performance and limits the application of MEMS gyroscopes in high-precision applications.Therefore,signal denoising becomes one of the important means to improve the accuracy of MEMS gyroscopes.In this paper,a filtering algorithm is proposed based on local mean decomposition(LMD)and adaptive least mean squares(LMS).Firstly,the output signal of MEMS gyroscope is decomposed using robust local mean decomposition,and then the PF components are categorized into mixed components and useful components by applying the multivariate permutation entropy,and then the mixed components are denoised by LMS to reconstruct the output signal of MEMS gyroscope.And experiments are con-ducted to verify the proposed algorithm,and the experimental results show that the noise mean and noise variance are significantly improved.
Keywords：MEMS gyroscopelocal mean decompositionmultivariate permutation entropyminimum mean square error al-gorithmintrinsic mode functionnoise reduction
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：6( 76-81 )