A Fusion Algorithm for Real-Time Temperature Compensation and Noise Suppression With a Double U-Beam Vibration Ring Gyroscope

With the rapid advancements in inertial technology and micro-electro-mechanical system (MEMS) fabrication, MEMS gyroscopes have gained widespread use across various industries. To address the problems of bad temperature stability and high noise in MEMS gyroscopes, this article proposes a novel fusion algorithm based on the double U-beam vibration ring gyroscope (DUVRG), which incorporates temperature compensation and noise suppression. The proposed algorithm constructs a gyro temperature error model using a signal extraction method based on quantized temperature. Noise suppression is achieved by integrating a Kalman filter (KF) and a statistical calibration filter (SCF) based on an adaptive sliding window (ASW). After three sets of temperature experiments with different temperature change rates, the compensated experimental results show that the gyro angular random walk (ARW) is reduced to 0.58°/
$\surd \text{h}$
and the bias instability (BI) is reduced to 1.10°/h in the range of −40 °C–60 °C. Compared with the original signals, the ARW and BI are reduced by 71.8% and 98.8%, respectively. The proposed novel temperature compensation and noise suppression method effectively enhances the temperature stability and noise performance of the gyroscope. Furthermore, this fusion algorithm requires less computational resources and satisfies the requirements of real-time signal processing, distinguishing it from conventional algorithms.