Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Song, L., Ni, J., Zhou, L., et al.: The analysis and simulation with the fatigue life of hemispherical resonator Gyro. J. Sens. 2021(2), 1–9 (2021)
Khanam, S., Dutt, J.K., Tandon, N.: Extracting rolling element bearing faults from noisy vibration signal using Kalman filter. J. Vibr. Acoust. 136(3), 031008 (2014)
Ding, M., Shi, Z., Du, B., et al.: A signal de-noising method for a MEMS gyroscope based on improved VMD-WTD. Meas. Sci. Technol. 32(9), 095112 (2021)
Zhou, X., Shan, D., Li, Q.: Morphological filter-assisted ensemble empirical mode decomposition. Math. Prob. Eng. 2018(PT.11), 1–12 (2018)
Vijayvargiya, A., Gupta, V., Kumar, R., et al.: A hybrid WD-EEMD sEMG feature extraction technique for lower limb activity recognition. IEEE Sens. J. 21(18), 20431–20439 (2021)
Chaitanya, B.K., Yadav, A., Pazoki, M.: An advanced signal decomposition technique for islanding detection in DG system. IEEE Syst. J. 15(3), 3220–3229 (2020)
Ying, W., Zheng, J., Pan, H., et al.: Permutation entropy-based improved uniform phase empirical mode decomposition for mechanical fault diagnosis. Digital Signal Process. 117, 103167 (2021)
Du, C., Xia, M., Peng, X., et al.: Detection algorithm for magnetic dipole target based on CEEMDAN and pattern recognition. Procedia Comput. Sci. 183, 669–676 (2021)
Li, S., Zhou, Q., Wu, S., et al.: Measurement of climate complexity using sample entropy. Int. J. Climatol. 26(15), 2131–2139 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chang, L. et al. (2023). Hemispherical Resonant Gyroscope Signal Denoising by CEEMDAN-WPLP. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_353
Download citation
DOI: https://doi.org/10.1007/978-981-19-6613-2_353
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-6612-5
Online ISBN: 978-981-19-6613-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)