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Purpose: To reduce the influence of temperature on MEMS gyroscope, this paper aims to propose a temperature drift compensation method based on variational modal decomposition (VMD), time-frequency peak filter (TFPF), mind evolutionary algorithm (MEA) and BP neural network. Design/methodology/approach: First, VMD decomposes gyro's temperature drift sequence to obtain multiple intrinsic mode functions (IMF) with different center frequencies and then Sample entropy calculates, according to the complexity of the signals, they are divided into three categories, namely, noise signals, mixed signals and temperature drift signals. Then, TFPF denoises the mixed-signal, the noise signal is directly removed and the denoised sub-sequence is reconstructed, which is used as training data to train the MEA optimized BP to obtain a temperature drift compensation model. Finally, the gyro's temperature characteristic sequence is processed by the trained model. Findings: The experimental result proved the superiority of this method, the bias stability value of the compensation signal is 1.279 × 10–3°/h and the angular velocity random walk value is 2.132 × 10–5°/h/vHz, which is improved compared to the 3.361°/h and 1.673 × 10–2°/h/vHz of the original output signal of the gyro. Originality/value: This study proposes a multi-dimensional processing method, which treats different noises separately, effectively protects the low-frequency characteristics and provides a high-precision training set for drift modeling. TFPF can be optimized by SEVMD parallel processing in reducing noise and retaining static characteristics, MEA algorithm can search for better threshold and connection weight of BP network and improve the model's compensation effect. [ABSTRACT FROM AUTHOR]

This article suggested two methods to compensate for the temperature drift of the micro-electro-mechanical system gyroscopes, which are support vector machine method and C-means support vector machine. The output of X axis which was ranged from −40°C to 60°C based on the micro-electro-mechanical system gyroscope is reduced and analyzed in this article. The results showed the correctness of the two methods. The final results indicate that when the temperature is ranged from −40°C to 60°C, the factor of B is reduced from 0.424 (° h) to 0.02194 (° h) , and when the temperature is ranged from 60°C to −40°C, the factor of B is reduced from 0.1056 (° h) to 0.0329 (° h) , and the temperature drift trend and noise characteristics are improved clearly. [ABSTRACT FROM AUTHOR]

This paper analyzes different noise components in MEMS gyroscope silicon structure, including mechanical–thermal noise (MTN), electronic-thermal noise (ETN), flicker noise (FN) and Coriolis signal in-phase noise (IPN). The structure equivalent electronic model is established, and the improved differential interface is proposed based on weak signal detection technology, after that, the noise components in silicon structure are introduced and analyzed in sense open loop. The quadrature error (QE) signal automatically cancellation loop is proposed, and the results of the experiment indicate that the equivalent angular rates of QE and IPN are 46°/s and 4.55°/s respectively. The interfaces contrast experiments show that the DC noise and the useful signal amplitudes of differential and single-side detection interfaces are −49.8 dBmV, −16.8 dBmV and −39.8 dBmV (−42.1 dBmV), −22.1 dBmV (−22.2 dBmV), which confirms the differential interface has better SNR. The carrier experiments also illustrate that higher carrier frequency (from 500 kHz to 10 MHz) can restrain DC noise (from −19.8 dBmV to −54.2 dBmV) better, which demonstrate the FN is the dominant noise component of the silicon structure under normal temperature. The temperature experiments show the DC noise enhances from −48.5 dBmV to −14.6 dBmV over the range 20 °C to 60°C while the useful signal amplitude remains around -16.6dBmV, and this phenomenon indicates the MTN and ETN become the dominant structure noise components gradually with temperature rising. [ABSTRACT FROM AUTHOR]

In recent years, High-G MEMS accelerometers have been widely used in aviation, medicine, and other fields. So it is extremely important to improve the accuracy and performance of High-G MEMS accelerometers. For this purpose, we propose a fusion algorithm that combines EMD, wavelet thresholding, and temperature compensation to process measurement data from a High-G MEMS accelerometer. In the fusion algorithm, the original accelerometer signal is first decomposed by EMD to obtain the intrinsic mode function (IMF). Then, sample entropy (SE) is used to divide the IMF components into three segments. The noise segment is directly omitted, wavelet thresholding is performed on the mixing segment, and a GA-BP performs temperature compensation on the drift segment. Finally, signal reconstruction is implemented. Later, a comparative analysis is carried out on the results from four models: EMD, wavelet thresholding, EMD + wavelet thresholding, and EMD + wavelet thresholding + temperature compensation. The experimental data show that the acceleration random walk change from 1712.66 g/h/Hz0.5 to 79.15 g/h/Hz0.5 and the zero-deviation stability change from 49275 g/h to 774.7 g/h. This indicates that the fusion algorithm (EMD + wavelet thresholding + temperature compensation) not only effectively suppresses the noise of high-frequency components but also compensates for temperature drift in the accelerometer. [ABSTRACT FROM AUTHOR]

A temperature error processing method for a dual-mass micro-electromechanical system (MEMS) gyroscope based on multi-scale parallel model is proposed. At first, a sample entropy based bounded ensemble empirical mode decomposition (SE-BEEMD) is proposed to decompose the original signal into different scales, include noise-only scale, mixed scale and drift scale; then forward linear prediction (FLP) is employed to eliminate the noise at mixed scale and extreme learning machine (ELM) based model is employed to compensate the drift at drift scale, the two steps are carried out paralleled; at last the final results can be obtained after reconstruction. Experimental results show that: (1) compared to tradition serial model, the proposed parallel model can eliminate the temperature errors more effectively, and each parameter of Allan analysis is improved. Specially, the quantification noise reduced from 0.035 μrad to 9.93e4 μrad, angle random walk reduced from 2.13e-5/s 1/2 to 7.94e-6/s 1/2 , bias instability reduced from 5.28e-4/s to 4.79e-4/s, rate random walk from 0.012/s 3/2 to 0.092/s 3/2 and angular rate ramp reduced from 0.013/s 2 to 0.011/s 2 ; (2) compared to traditional time consuming neural networks, the ELM has the best modeling accurate and shortest training time, which would be valuable for online temperature drift modeling and compensation. [ABSTRACT FROM AUTHOR]