MEMS gyroscope wavelet de-noising method based on redundancy and sparse representation

Since the wavelet analysis does not need exact noise model, wavelet decomposition is suitable for analyzing the signal with complex noise characteristics. In this paper, a real-time wavelet de-noising method is used for the error compensation of MEMS gyroscope. In order to avoid the shortcomings that wavelet threshold processing ignores the wavelet coefficient integrity, the sparse and redundant representation methods are used to optimize the wavelet coefficients. The reverse use of compressed sensing algorithm represents the low-frequency wavelet coefficients redundantly, and optimizes the sparse coefficients, and then reconstructs the low-frequency wavelet coefficients. The sparse representation method is used to sparsely represent the high-frequency wavelet coefficients, and the high-frequency wavelet coefficients are optimized. Then the optimized high and low frequency wavelet coefficients are used to reconstruct the original signal and the lag correction algorithm is used to reduce the boundary effect of wavelet decomposition. Finally, the feasibility of the algorithm is verified by simulation and experiments, which provides a new idea for wavelet de-noising.