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
On-line prístup:	Získať plný text
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