A New Signal Denoising Algorithm for Low Cost MEMS Gyroscope

Inertial measurement unit (IMU) has been commonly used in measuring angles, displacement measurement and other fields. However, micro electro-mechanical system (MEMS) gyroscopes have little accuracy of measuring and enormous noise; real signals will be cloaked in the noise. A new denoising algorithm for low cost MEMS gyroscope is offeredin this paper. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is used to decompose the initial-signal which can obtain a limited number of intrinsic mode functions (IMF). Next, use the power spectrum entropy to divide the IMF into noise signals, mixed signals and sound signals. Then, variational mode decomposition (VMD) is choosen to decompose the mixed signal, and using reptile search algorithm (RSA) optimization algorithm optimizes the crucial arguments of VMD. VMD algorithm appropriately dismantles the signal according to the optimized arguments to attain the IMF sections, divided into signal sections and noise sections using the Bhattacharyian distance. The signal sections are furthermore processed by lifting wavelet threshold denoising (LWT), and the valuable signal with denoising is obtained. Finally, all the valuable signals are amalgamated and reestablished into the ending-denoising signal. The effectuality of the algorithm is weighted by Matlab simulation. The static denoising experimentation of the three-axis turntable is put up in the experimental analysis, which exhaustively confirms that the algorithm has clear superiority in denoising and significantly increases the signal quality and the accuracy of the cheap MEMS gyroscope.