Research on ICEEMDAN-MEMS Gyroscopic Signal Processing Based on Parameter Optimization
In extreme application environments of multi-dimensional intelligent perception systems for aircraft,the output signal of MEMS gyroscopes will contain various noises,which will affect the subsequent navigation tasks of the system.In order to improve the quality of MEMS gyroscope output signals,an improved adaptive noise fully integrated empirical mode decomposition(ICEEM-DAN)MEMS gyroscope signal filtering method with parameter optimization is proposed.Firstly,optimize the parameters of ICEEM-DAN using the Grey Wolf Optimization Algorithm(GWO);Decompose the output signal of MEMS gyroscopes to obtain several intrin-sic mode functions(IMFs),divide the components based on permutation entropy,and use recursive least squares algorithm(RLS)to filter the mixed components;Finally,the signal is reconstructed to obtain the final signal.The experimental data of the system was processed,and the analysis results showed that compared with the original signal,the root mean square error(RMSE)of the output signal of the MEMS gyroscope after noise reduction was reduced by 78.6％.This algorithm can effectively remove noise from the out-put signal and has certain engineering application value.
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