Abstract
This paper presents a bias drift self-calibration method for micro-electromechanical systems (MEMS) gyroscopes based on noise-suppressed mode reversal without the modeling of bias drift signal. At first, the bias drift cancellation is accomplished by periodic switching between operation mode of two collinear gyroscopes and subtracting the bias error which is estimated by the rate outputs from a consecutive period interval; then a novel filtering algorithm based on improved complete ensemble empirical mode decomposition (improved complete ensemble empirical mode decomposition with adaptive noise—CEEMDAN) is applied to eliminate the noise in the calibrated signal. A set of intrinsic mode functions (IMFs) is obtained by the decomposition of the calibrated signal using improved CEEMDAN method, and the threshold denoising method is utilized; finally, the de-noised IMFs are reconstructed into the desired signal. To verify the proposed method, the hardware circuit with an embedded field-programmable gate array (FPGA) was implemented and applied in bias drift calibration for the two MEMS gyroscopes manufactured in our laboratory. The experimental results indicate that the proposed method is feasible, and it achieved a better performance than the typical mode reversal. The bias instability of the two gyroscopes decreased from 0.0066 and 0.0055 to 0.0011; and, benefiting from the threshold denoising based on improved CEEMDAN, the angle random walks decreased from 1.18 and 2.04 to 2.19 , respectively.
Highlights
Micro-electromechanical system (MEMS) gyroscopes are developing rapidly due to their extensive applications in the field of consumer electronics and in the military, such as game stations, cameras, vehicle control systems and weapon guidance
We propose a bias drift self-calibration method for micro-electromechanical systems (MEMS) gyroscopes based on noise-suppressed mode reversal, and this method does not require modeling of the bias drift signal
The proposed method is advantageous over the previous method in that: (1) it has a relatively simple control system to ensure an acceptable bandwidth and can work without modeling of bias drift signal; (2) the noise performance of rate outputs has been significantly improved by utilizing the threshold denoising based on an improved CEEMDAN
Summary
Micro-electromechanical system (MEMS) gyroscopes are developing rapidly due to their extensive applications in the field of consumer electronics and in the military, such as game stations, cameras, vehicle control systems and weapon guidance. These mode reversal methods for MEMS gyroscope periodically switched the resonator of drive and sensing mode to obtain an estimate of angular rate without bias drift, just like correlated double sampling in circuits. The proposed self-calibration method: (1) uses mode reversal method and continuously samples the rate output to extract the measurement signal; (2) uses a linear combination algorithm to obtain an estimate of bias error and the compensated rate output; (3) decomposes the rate output signal by an improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); and (4) utilizes threshold denoising algorithm to filter the decomposition results and reconstructs them to obtain the desired rate outputs. The proposed method is advantageous over the previous method in that: (1) it has a relatively simple control system to ensure an acceptable bandwidth and can work without modeling of bias drift signal; (2) the noise performance of rate outputs has been significantly improved by utilizing the threshold denoising based on an improved CEEMDAN. The implementation of the proposed self-calibration system and the relevant experimental testing is given in Section 3; Section 4 concludes the whole paper
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