A Novel Noise Reduction Algorithm of MEMS Gyroscope Based on Compressive Sensing and Lifting Wavelet Transform

Wen Jie Zhu; Guang Long Wang; Zhong Tao Qiao; Feng Qi Gao

doi:10.4028/www.scientific.net/KEM.609-610.1138

Paper Titles

Design, Fabrication and Test of In-Plane MEMS Piezoresistive High-g Accelerometer
p.1111

The Design of Lead Compensator for ΣΔ Micromachined Accelerometer
p.1117

High Precision Double-Interferometry for Large Step-Height On-Line Measurement Using Waveform Transforming Technology
p.1122

Accuracy Analysis of Quality Factor Measurement Based on Time Domain Method for MEMS Resonators
p.1131

A Novel Noise Reduction Algorithm of MEMS Gyroscope Based on Compressive Sensing and Lifting Wavelet Transform
p.1138

Micro-Machined Gyroscope Mathematical Model and Error Analysis
p.1144

Finite Element Analysis on Thermosonic Ball Bonding Process of MEMS Chip
p.1153

Lateral Resolution Test for Confocal Laser Scanning Microscope
p.1159

Fabrication and Packaging of Electromagnetic 2D MEMS Scanning Mirror
p.1165

HomeKey Engineering MaterialsKey Engineering Materials Vols. 609-610A Novel Noise Reduction Algorithm of MEMS...

A Novel Noise Reduction Algorithm of MEMS Gyroscope Based on Compressive Sensing and Lifting Wavelet Transform

Abstract:

A novel noise reduction algorithm combined with compressive sensing (CS) and lifting wavelet transform (LWT) is proposed in this paper. This algorithm can overcome the limitations of traditional noise reduction methods based on Kalman filtering and wavelet threshold filtering. The characteristics of wavelet time-frequency distribution of the microelectromechanical system (MEMS) gyroscope are discussed to illustrate the demerit of the classical filtering methods. Noise reduction algorithm of MEMS gyroscope signal is studied in detail by combining CS theory with lifting wavelet transform. De-noising effect, time-consumption of computation as well as traditional CS reconstruction algorithms are analyzed. The results show that the signal reconstruction algorithm of conventional matching pursuit (MP) greedy algorithms contains more glitches and computation time-consumption, the basis pursuit de-noising (BPDN) algorithm is better and it has advantages of high computational efficiency and ease of implementation.

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Periodical:

Key Engineering Materials (Volumes 609-610)

Pages:

1138-1143

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.609-610.1138

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Online since:

April 2014

Authors:

Wen Jie Zhu*, Guang Long Wang, Zhong Tao Qiao, Feng Qi Gao

Keywords:

Compressive Sensing, De-Noising, Lifting Wavelet Transform, MEMS Gyroscope, Reconstruction

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