Citation: Huo, Z.; Wang, F.; Shen, H.;
Sun, X.; Zhang, J.; Li, Y.; Chu, H.
Optimal Compensation of MEMS
Gyroscope Noise Kalman Filter
Based on Conv-DAE and
MultiTCN-Attention Model in Static
Base Environment. Sensors 2022, 22,
7249. https://doi.org/10.3390/
s22197249
Academic Editors: M. Jamal Deen,
Subhas Mukhopadhyay, Yangquan
Chen, Simone Morais, Nunzio
Cennamo and Junseop Lee
Received: 19 July 2022
Accepted: 16 September 2022
Published: 24 September 2022
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Article
Optimal Compensation of MEMS Gyroscope Noise Kalman
Filter Based on Conv-DAE and MultiTCN-Attention Model in
Static Base Environment
Zimin Huo
1,2
, Fuchao Wang
3
, Honghai Shen
3
, Xin Sun
1,2
, Jingzhong Zhang
4
, Yaobin Li
1,2,
*
and Hairong Chu
1,
*
1
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences,
Changchun 130033, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics,
Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
4
Forest Protection Research Institute of Heilongjiang Province, Harbin 150040, China
* Correspondence: liyaobin@ciomp.ac.cn (Y.L.); chuhr@ciomp.ac.cn (H.C.)
Abstract: Errors in microelectromechanical systems (MEMS) inertial measurement units (IMUs) are
large, complex, nonlinear, and time varying. The traditional noise reduction and compensation
methods based on traditional models are not applicable. This paper proposes a noise reduction
method based on multi-layer combined deep learning for the MEMS gyroscope in the static base state.
In this method, the combined model of MEMS gyroscope is constructed by Convolutional Denoising
Auto-Encoder (Conv-DAE) and Multi-layer Temporal Convolutional Neural with the Attention
Mechanism (MultiTCN-Attention) model. Based on the robust data processing capability of deep
learning, the noise features are obtained from the past gyroscope data, and the parameter optimization
of the Kalman filter (KF) by the Particle Swarm Optimization algorithm (PSO) significantly improves
the filtering and noise reduction accuracy. The experimental results show that, compared with the
original data, the noise standard deviation of the filtering effect of the combined model proposed
in this paper decreases by 77.81% and 76.44% on the x and y axes, respectively; compared with
the existing MEMS gyroscope noise compensation method based on the Autoregressive Moving
Average with Kalman filter (ARMA-KF) model, the noise standard deviation of the filtering effect
of the combined model proposed in this paper decreases by 44.00% and 46.66% on the x and y axes,
respectively, reducing the noise impact by nearly three times.
Keywords:
MEMS gyroscope; convolutional denoising autoencoder; temporal convolutional
network
;
attention mechanism; Particle Swarm Optimization algorithm; Kalman filter
1. Introduction
MEMS gyroscopes have the characteristics of small size, low power consumption,
low cost, and high-cost performance [
1
]. It is easier to act as an actuator or a key node of
inertial navigation in small institutions, such as in the drone remote sensing measurement
gimbals [
2
], aviation pods [
3
,
4
], navigation terminals [
5
,
6
], and other institutions, and it
plays an important role. High-precision MEMS gyroscopes can already meet the needs of
engineers for practical projects, so reducing the noise of MEMS gyroscopes and improving
measurement accuracy has become a hot issue.
Traditional gyroscope noise reduction methods include Kalman filter [
7
], Fast Fourier
Transform [
8
], Empirical Mode Decomposition [
9
], Wavelet Transform [
10
], Variational
Mode Decomposition [
11
], and Ensemble Empirical Mode Decomposition [
12
], etc. For
example, Liu, Fuchao [
13
] proposed an adaptive unscented Kalman filter algorithm by
analyzing the influence of the MEMS IMU noise statistical characteristics on the accuracy
Sensors 2022, 22, 7249. https://doi.org/10.3390/s22197249 https://www.mdpi.com/journal/sensors
