To read the full version of this content please select one of the options below:

Dual mass MEMS gyroscope temperature drift compensation based on TFPF-MEA-BP algorithm

Huiliang Cao (Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Tai Yuan, China)
Rang Cui (Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Tai Yuan, China)
Wei Liu (Jinzhou Jinheng Automotive Safety System Co., Ltd, Jinzhou, China)
Tiancheng Ma (Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Tai Yuan 030051, China)
Zekai Zhang (Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Tai Yuan 030051, China)
Chong Shen (Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Tai Yuan 030051, China)
Yunbo Shi (Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Tai Yuan 030051, China)

Sensor Review

ISSN: 0260-2288

Article publication date: 8 April 2021

Issue publication date: 17 May 2021

219

Abstract

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.

Keywords

Acknowledgements

Conflicts of Interest: The authors declare no conflict of interest.

Citation

Cao, H., Cui, R., Liu, W., Ma, T., Zhang, Z., Shen, C. and Shi, Y. (2021), "Dual mass MEMS gyroscope temperature drift compensation based on TFPF-MEA-BP algorithm", Sensor Review, Vol. 41 No. 2, pp. 162-175. https://doi.org/10.1108/SR-09-2020-0205

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

Related articles