Optimal Compensation of MEMS Gyroscope Noise Kalman Filter Based on Conv-DAE and MultiTCN-Attention Model in Static Base Environment

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-laye...

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Vydané v:Sensors (Basel, Switzerland) Ročník 22; číslo 19; s. 7249
Hlavní autori: Huo, Zimin, Wang, Fuchao, Shen, Honghai, Sun, Xin, Zhang, Jingzhong, Li, Yaobin, Chu, Hairong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 24.09.2022
MDPI
Predmet:
Accuracy
Algorithms
Analysis
Artificial intelligence
attention mechanism
convolutional denoising autoencoder
Deep learning
Kalman filter
Kalman filters
Mathematical optimization
MEMS gyroscope
Microelectromechanical systems
Network topologies
Neural networks
Noise control
Optimization algorithms
Parameter estimation
Particle Swarm Optimization algorithm
Sensors
Support vector machines
temporal convolutional network
Time series
Wavelet transforms
China
ISSN:1424-8220, 1424-8220
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Popis
Shrnutí: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22197249