ArticleMicromachines2024
Hybrid Filtering Compensation Algorithm for Suppressing Random Errors in MEMS Arrays.
Article in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Performance Analysis of Typical Data Fusion Algorithms for Inertial Measurement Arrays.Micromachines · 2026Article
- A Novelty Temperature Compensation Model for Dual-Mass Vibration MEMS Gyroscope Based on Machine Learning and TTAO-VMD Algorithm.Micromachines · 2026Article
- Sliding window constrained fault-tolerant filtering of compressor vibration data.Royal Society open science · 2025Article
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Authors and funding
4 authors.
Funding
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Abstract
To solve the high error phenomenon of microelectromechanical systems (MEMS) due to their poor signal-to-noise ratio, this paper proposes an online compensation algorithm wavelet threshold back-propagation neural network (WT-BPNN), based on a neural network and designed to effectively suppress the random error of MEMS arrays. The algorithm denoises MEMS and compensates for the error using a back propagation neural network (BPNN). To verify the feasibility of the proposed algorithm, we deployed it in a ZYNQ-based MEMS array hardware. The experimental results showed that the zero-bias instability, angular random wander, and angular velocity random wander of the gyroscope were improved by about 12 dB, 10 dB, and 7 dB, respectively, compared with the original device in static scenarios, and the dispersion of the output data was reduced by about 8 dB in various dynamic environments, which effectively verified the robustness and feasibility of the algorithm.
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