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Wavelet Analysis Based Signal Denoising Research in MEMS Gyroscope

Author: LiuYong
Tutor: LuoBing
School: National University of Defense Science and Technology
Course: Control Science and Engineering
Keywords: MEMS gyroscope Wavelet Analysis Signal Denoising Standard deviation
CLC: TN911.4
Type: Master's thesis
Year: 2011
Downloads: 66
Quote: 0
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Abstract


Gyro drift is the main error sources of inertial navigation system error, and therefore effectively compensate for gyro drift error is the key to ensure the accuracy of the inertial navigation system. The MEMS gyro random drift error is often manifested in the non-stationary, weak linear, slow time-varying characteristics, influenced by the uncertainties of the external environment, can not be compensated for with a simple inertial navigation system. Wavelet analysis of its excellent resolution characteristics particularly suitable for the treatment of non-stationary signals, research from the characteristics of the MEMS gyro signal denoising based on wavelet analysis method applied to the MEMS gyro signal noise reduction. Mainly on the wavelet transform modulus maxima noise reduction methods, the correlation denoising method based on wavelet transform scale wavelet thresholding denoising method study, a comparative analysis of the effect of the actual application process. The main content of the paper include: analysis and summarizes the context of the wavelet transform method, background knowledge and its application of MEMS gyro signal noise reduction; elaborated the continuous wavelet transform, discrete wavelet transform, the basic theory of dyadic wavelet transform wavelet multi-resolution analysis theory; highlights a method based on wavelet transform modulus maxima noise reduction, the Denoising correlation between the scale and the Wavelet threshold denoising method principle, the realization of the algorithm, and method of characteristics. MEMS gyroscope simulated signals generated by the MATLAB object use modulus maxima denoising method based on wavelet analysis scale the correlation noise reduction method and wavelet threshold denoising method is its noise reduction process, and draw optimal wavelet basis of the above three methods and the optimal noise reduction scale. Wherein Birge-Massart threshold gyro simulation signal 12 scale noise reduction achieved the best results, almost complete suppression of the noise signal, retained 94.91% of the signal energy, so that the standard deviation of the signal is reduced from 1.3270 (° / s) 0.0477 (° / s), to improve the stability of the signal. Test system based on MEMS gyroscope silicon micro gyro calibration experiments were conducted to obtain a silicon micro-gyroscope static, uniform rotation and variable speed rotation angular velocity data. The parameters selected conclusions based on the simulation of signal noise reduction in MALAB noise reduction processing based on wavelet analysis, respectively, to the measured data. Through the analysis of the results of the three methods of noise reduction, it is best able to almost completely suppress the noise and retain a higher proportion of the energy Birge-Massart threshold denoising method for noise reduction effect. Meanwhile, Birge-Massart threshold denoising method poor standard of gyro static signal is reduced to 0.0069 from 0.1987 (° / s) (° / s), the standard deviation of the uniform rotation signal is reduced to 0.0138 from 0.2169 (° / s) ( ° / s), the amplitude of the canceling signal to shift rotation signal does not cause a large attenuation, noise can be almost completely inhibited. In this paper, the noise reduction processing on the MEMS gyroscope simulated signals and measured data show that the use of noise reduction method based on wavelet analysis, better value in improving the performance of MEMS gyroscope.

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