Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN

Abstract

Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s2 to −1.2646 × 10−7 m/s2, while the variance decreases from 0.0133 m/s2 to 1.0975 × 10−5 m/s2. In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals.

A new signal denoising algorithm for low-cost MEMS gyroscope

“…However, in actual engineering, it is impossible to conduct a large number of data acquisition experiments for a single sensor, resulting in unsatisfactory compensation effects. For such problems, literature [5][6] tried to reduce the amount of data by using different neural networks but did not fundamentally solve the problem. Reference [7] aimed at the severe mode mixing appearance in the traditional empirical mode decomposition process, using the scheme of the lifting wavelet coupled with CEEMDAN for signal denoising and achieved satisfactory results.…”