MEMS Gyro Signal De-Noising Based on Adaptive Stationary Wavelet Threshold

Xing Hui Yang; Jian Xin Ren; Xing Mei Zhao; Ran Chen

doi:10.4028/www.scientific.net/AMR.466-467.986

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 466-467MEMS Gyro Signal De-Noising Based on Adaptive...

MEMS Gyro Signal De-Noising Based on Adaptive Stationary Wavelet Threshold

Abstract:

Random drift is a significant index that can affect the precision of MEMS gyroscope. It is one of the important techniques to decrease the random drift error in improving the precision of MEMS gyro. According to analyzing the principle of traditional Wavelet Transform and Stationary Wavelet Transform, Stationary Wavelet Transform (SWT) is adopted to de-noise the signal of MEMS gyro. Due to SWT’s time-invariant, the Gibbs phenomenon is decreased. SWT with adaptive threshold is adopted to analyze the actual dynamic MEMS gyroscope data. The experimental results show that this presented method is better than traditional wavelet threshold de-noising methods. It can effectively restrain the noise in high frequency and improve the drift error of MEMS gyro.

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Periodical:

Advanced Materials Research (Volumes 466-467)

Pages:

986-990

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.466-467.986

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Online since:

February 2012

Authors:

Xing Hui Yang, Jian Xin Ren, Xing Mei Zhao, Ran Chen

Keywords:

Adaptive Threshold, MEMS Gyroscope, Stationary Wavelet Transform

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References

[1] JI Xun-sheng, WANG Shou-rong. Application of the Wavelet Transformation in the MEMS Gyroscope De-Noise, J. CHINESE JOURNAL OF SENSORS AND ACTUATORS. 2006, 19(01): 150-152.

Google Scholar

[2] A. Manjunath, H.M. Ravikumar. Comparison of Discrete Wavelet Transform (DWT), Lifting Wavelet Transform (LWT) Stationary Wavelet Transform (SWT) and S-Transform in Power Quality Analysis, J. European Journal of Scientific Research. pp.569-576, April (2010).

DOI: 10.7324/ijcrr.2017.9129

Google Scholar

[3] Zheng Chuanxing, Zhang Yiming. Noise Reduction for Low-field Pulsed NMR Signal via Stationary Wavelet Transform. IEEE The Eighth International Conference on Electronic Measurement and Instruments 2007 3-746-750.

DOI: 10.1109/icemi.2007.4351025

Google Scholar

[4] ZHANG Geng, MA Jiancang. De-noising of MEMS Gyroscope Based on Wavelet and Adaptive Threshold, J. Journal of Projectiles, Rockets, Missiles and Guidance. 2008, 28(05): 64-66.

Google Scholar

[5] ZHAO Li-ye, WANG Shou-rong. Silicon Micro-Gyroscope Signal Processing method Based on Adaptive Wavelet Thresholding, J. The Micro Computer Information. 2009, 25(12-2): 4-6.

Google Scholar