A combined method for mems gyroscope error compensation using a long short-term memory network and kalman filter in random vibration environments

doi:10.3390/s21041181

CIOMP OpenIR

	A combined method for mems gyroscope error compensation using a long short-term memory network and kalman filter in random vibration environments
	C. Zhu; S. Cai; Y. Yang; W. Xu; H. Shen and H. Chu
	2021
发表期刊	Sensors (Switzerland)
ISSN	14248220
卷号	21 期号:4 页码:1-21
摘要	In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degrada-tion of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
DOI	10.3390/s21041181
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收录类别	SCI ; EI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/65097
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	C. Zhu,S. Cai,Y. Yang,et al. A combined method for mems gyroscope error compensation using a long short-term memory network and kalman filter in random vibration environments[J]. Sensors (Switzerland),2021,21(4):1-21.
APA	C. Zhu,S. Cai,Y. Yang,W. Xu,&H. Shen and H. Chu.(2021).A combined method for mems gyroscope error compensation using a long short-term memory network and kalman filter in random vibration environments.Sensors (Switzerland),21(4),1-21.
MLA	C. Zhu,et al."A combined method for mems gyroscope error compensation using a long short-term memory network and kalman filter in random vibration environments".Sensors (Switzerland) 21.4(2021):1-21.

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