Noise Reduction Method for Maglev Gyroscope Signals
Author Information
Author(s): Liu Di, Shi Zhen, Yang Ziyi, Zou Chenxi
Primary Institution: School of Geology Engineering and Geomatics, Chang’an University
Hypothesis
This study proposes a noise reduction method that integrates an adaptive particle swarm optimization variational mode decomposition algorithm with a strategy for error compensation of the trend term in reconstructed signals.
Conclusion
The proposed method significantly improves the azimuth measurement accuracy of the maglev gyroscope, reducing measurement error by an average of 45.63%.
Supporting Evidence
- The method reduced the average standard deviation of the compensated signals by 46.10%.
- The average measurement error of the north azimuth was reduced by 45.63%.
- The noise reduction performance surpassed that of four other algorithms.
Takeaway
This study found a way to make gyroscope measurements more accurate by reducing noise from the environment, which helps the gyroscope work better.
Methodology
The study used an adaptive particle swarm optimization variational mode decomposition algorithm combined with error compensation for signal reconstruction.
Limitations
The study primarily focuses on noise reduction under continuous external environmental interference and may not address other types of noise.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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