MEMS gyroscope array denoising method based on VMD and MPSO-ELM
By constructing a denoising method for MEMS gyroscope arrays based on VMD and MPSO-ELM, the problem of poor denoising effect and low efficiency in MEMS gyroscope arrays is solved, and a high-efficiency denoising effect is achieved, which is applicable to inertial navigation and vibration analysis.
CN121614735APending Publication Date: 2026-03-06ZHONGBEI UNIV
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Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-03
- Publication Date
- 2026-03-06
AI Technical Summary
Technical Problem
Existing denoising methods are ineffective and have low efficiency in MEMS gyroscope arrays.
Method used
The method based on VMD and MPSO-ELM is adopted. By constructing a MEMS gyroscope array, using 10s smoothed variance for signal weighted fusion, using the VMD algorithm to decompose the signal, using sample entropy to divide the components, constructing an MPSO-ELM network for training, establishing the input-output mapping relationship, and outputting a denoised signal.
Benefits of technology
It significantly improves the denoising effect of MEMS gyroscope arrays, increases denoising efficiency, and reduces zero-bias instability, angle and rate random walks.
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Figure CN121614735A_ABST
Abstract
The invention relates to the technical field of MEMS gyroscopes, in particular to an MEMS gyroscope array denoising method based on VMD and MPSO-ELM, and the method is realized by adopting the following steps: step 1, collecting output signals of each MEMS gyroscope; 2, carrying out weighted fusion on the output signals of the MEMS gyroscopes; 3, decomposing the fusion signal; 4, dividing each intrinsic mode function into a useful component and a noise component, removing the noise component, and then recombining the useful component; step 5, an MPSO-ELM network is constructed; 6, dividing the recombined signal into a training set and a verification set, and training the MPSO-ELM network by using the training set; step 7, establishing an input and output mapping relation of the MPSO-ELM network; and step 8, inputting the verification set into the MPSO-ELM network, thereby outputting a predicted value which is a de-noised signal. The method solves the problems that when an existing denoising method is applied to the MEMS gyroscope array, the denoising effect is poor, and the denoising efficiency is low, and is suitable for the fields of inertial navigation, vibration analysis and the like.
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