Research on adaptive modulus maxima selection of wavelet modulus maxima denoising

Microelectromechanical system (MEMS) accelerometers are small in size, low in power consumption and easily integrated. They can be used in intelligent hydraulic components to obtain the dynamic acceleration of the system and monitor the operating status of the system. In this study, based on the noise characteristics of MEMS accelerometer output signals, a wavelet modulus maxima denoising algorithm based on adaptive threshold estimation is proposed, in which SureShrink threshold estimation is used to choose the right modulus maxima. Then, the signal-to-noise ratio and mean-square-error are used as the evaluating indices of the denoising performance for the wavelet modulus maxima denoising based on SureShrink threshold estimation, the wavelet modulus maxima denoising based on BayesShrink threshold estimation and the normal wavelet modulus maxima denoising. The simulation results show that the wavelet modulus maxima denoising based on SureShrink threshold estimation has better denoising performance than the normal modulus maxima denoising and the wavelet modulus maxima method based on BayesShrink threshold estimation, and effectively eliminates the noise of MEMS accelerometer output signals.