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 paper 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°/√h and the bias instability (BI) is reduced to 1.10°/h in the range of -40°C to 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.

In order to remove noise generated during the accelerometer calibration process, an accelerometer denoising method based on empirical mode decomposition (EMD) and time-frequency peak filtering (TFPF) is proposed in this paper. Firstly, a new design of the accelerometer structure is introduced and analyzed by finite element analysis software. Then, an algorithm combining EMD and TFPF is proposed for the first time to deal with the noise of the accelerometer calibration process. Specific steps taken are to remove the intrinsic mode function (IMF) component of the high frequency band after the EMD decomposition, and then to use the TFPF algorithm to process the IMF component of the medium frequency band; meanwhile, the IMF component of the low frequency band is reserved, and finally the signal is reconstructed. The reconstruction results show that the algorithm can effectively suppress the random noise generated during the calibration process. The results of spectrum analysis show that EMD + TFPF can effectively protect the characteristics of the original signal and that the error can be controlled within 0.5%. Finally, Allan variance is used to analyze the results of the three methods to verify the filtering effect. The results show that the filtering effect of EMD + TFPF is the most obvious, being 97.4% higher than the original data.