TY - GEN
T1 - Hemispherical Resonant Gyroscope Signal Denoising by CEEMDAN-WPLP
AU - Chang, Longkang
AU - Zhang, Guochang
AU - Zhang, Ya
AU - Gao, Wei
AU - Wei, Jianxiong
AU - Shao, Jianbo
AU - Jiang, Pan
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - Hemispherical resonator gyroscope (HRG) has been widely used in strap-down inertial navigation systems. However, the output noise of HRG will degrade the precision of SINS seriously. To reduce the impacts of noise on HRG accuracy, an improved hybrid denoising method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet packet transform-forward linear prediction (WPLP) filter algorithm is proposed in this article. There are three steps in this algorithm: first of all, in this study, the CEEMDAN approach is given for decomposing the HRG output signal into different intrinsic mode functions (IMFs); secondly, these IMFs are divided into three categories by the sample entropy (SE), which is pure noise portion, hybrid portion, and a residual portion. Meanwhile, the pure portion is removed off directly and the hybrid portion is filtered employing the WPLP filter. Ultimately, the final signal is reconstructed. An actual experiment was carried out and the findings demonstrate that the suggested CEEMDAN-WPLP method effectively reduces the HRG output noise, which the angular random walk and the bias stability are optimized by 99.8 % and 68.3 % respectively; further, by comparing with other algorithms, the superiority of the suggested method is demonstrated.
AB - Hemispherical resonator gyroscope (HRG) has been widely used in strap-down inertial navigation systems. However, the output noise of HRG will degrade the precision of SINS seriously. To reduce the impacts of noise on HRG accuracy, an improved hybrid denoising method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet packet transform-forward linear prediction (WPLP) filter algorithm is proposed in this article. There are three steps in this algorithm: first of all, in this study, the CEEMDAN approach is given for decomposing the HRG output signal into different intrinsic mode functions (IMFs); secondly, these IMFs are divided into three categories by the sample entropy (SE), which is pure noise portion, hybrid portion, and a residual portion. Meanwhile, the pure portion is removed off directly and the hybrid portion is filtered employing the WPLP filter. Ultimately, the final signal is reconstructed. An actual experiment was carried out and the findings demonstrate that the suggested CEEMDAN-WPLP method effectively reduces the HRG output noise, which the angular random walk and the bias stability are optimized by 99.8 % and 68.3 % respectively; further, by comparing with other algorithms, the superiority of the suggested method is demonstrated.
KW - Denoising
KW - Hemispherical resonator gyroscope (HRG)
KW - Wavelet packet transform-forward linear prediction (WPLP)
UR - https://www.scopus.com/pages/publications/85151159094
U2 - 10.1007/978-981-19-6613-2_353
DO - 10.1007/978-981-19-6613-2_353
M3 - 会议稿件
AN - SCOPUS:85151159094
SN - 9789811966125
T3 - Lecture Notes in Electrical Engineering
SP - 3633
EP - 3643
BT - Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
A2 - Yan, Liang
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2022
Y2 - 5 August 2022 through 7 August 2022
ER -