An MEMS gyro denoising method based on a wavelet threshold

Abstract

The invention discloses an MEMS gyro denoising method based on a wavelet threshold, which comprises the following steps of sampling a MEMS gyro signal; performing the trend analysis on the MEMS gyro signal; determining the wavelet bases and decomposition layers based on the results of trend analysis, selecting the wavelet coefficient transform function based on the determined wavelet basis and decomposition layer number, and distinguishing the noise and detail of the signal based on the selected wavelet coefficient transform function, selecting threshold functions and thresholds to remove thenoise components from the detailed part of the signal. By analyzing the output signal of MEMS gyroscope, the drift of MEMS gyroscope has a linear trend, which is a slow-changing process. The analysisshows that the output signal of MEMS gyroscope is a stationary signal with low frequency. On the basis of analysis, the influence of random error of MEMS gyroscope is reduced, and the real-time outputprecision of MEMS gyroscope sensor is improved. Because only wavelet coefficient transform is used, the calculation is simple, is easy to realize and has the advantage of high precision.

Description

technical field [0001] The invention relates to the field of random error compensation of MEMS micromechanical gyroscopes in integrated navigation, in particular to a method for denoising MEMS gyroscopes based on wavelet thresholds. Background technique [0002] At present, the output signal of the MEMS gyroscope is a typical non-stationary sequence, and in actual use, the error will continue to accumulate due to the complex and changeable environment, and the random error of the gyroscope is the main reason affecting the accuracy of the MEMS gyroscope. [0003] Common MEMS gyroscope noise reduction methods include modeling compensation, artificial intelligence algorithm and wavelet transform algorithm, etc. The general modeling compensation method relies too much on the established model and prior statistical information, and the fault tolerance is not only bad but also inaccurate statistics will cause this method to fail. Although the artificial intelligence neural networ...

AI Technical Summary

Problems solved by technology

The general modeling compensation method relies too much on the established model and prior statistical information, and the fault tolerance is not only bad but also inaccurate statistics will cause this method to fail

Although the artificial intelligence neural network algorithm has a certain degree of fault tolerance, its structure is huge, and it is not suitable for the calculation and processing of the gyro noise reduction part in attitude estimation.

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