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2023 | Vol. 30, nr 4 | 755--772
Tytuł artykułu

A hybrid denoising method for gyroscopes based on multiple screening

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Języki publikacji
EN
Abstrakty
EN
To reduce the random error of microelectromechanical system (MEMS) gyroscope, a hybrid method combining improved empirical mode decomposition (EMD) and least squares algorithm (LS) is proposed. Firstly, based on the multiple screening mechanism, intrinsic mode functions (IMFs) from the first decomposition are divided into noise IMFs, strong noise mixed IMFs, weak noise mixed IMFs and signal IMFs. Secondly, according to their characteristics, they are processed again. IMFs from the second decomposition are divided into noise IMFs and signal IMFs. Finally, useful signal is gathered to obtain the final denoising signal. Compared with some other denoising methods proposed in recent years, the experimental results show that the proposed method has obvious advantages in suppressing random error, greatly improving the signal quality and improving the accuracy of inertial navigation.
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Rocznik
Strony
755--772
Opis fizyczny
Bibliogr. 22 poz., rys., tab., wykr., wzory
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autor
autor
autor
autor
Bibliografia
  • [1] Shen, C., Li, J., Zhang, X., Shi, Y., Tang, J., Cao, H., & Liu, J. (2016). A noise reduction method for Dual-Mass Micro-Electromechanical gyroscopes based on sample entropy empirical mode decomposition and Time-Frequency peak filtering. Sensors, 16(5), 796. https://doi.org/10.3390/s16060796
  • [2] Hesham, M. A., Ibrahim, M. E., & Sami, A. E. (2022). Noise reduction in optical gyroscope signals based on hybrid approaches. Journal of Optics, 51(1), 5-21. https://doi.org/10.1007/s12596-020-00617-3
  • [3] Xiaoting, G., Changku, S., Peng, W., & Lu, H. (2018). Hybrid methods for MEMS gyro signal noise reduction with fast convergence rate and small steady-state error. Sensors and Actuators A: Physical, 269, 145-159. https://doi.org/10.1016/j.sna.2017.11.013
  • [4] Shuo, C., Yunfeng, H., Haitao, D., & Hong, C. (2018). A Noise reduction method for MEMS gyroscope based on direct modeling and Kalman filter. IFAC-PapersOnLine, 51(31), 172-176. https://doi.org/10.1016/j.ifacol.2018.10.032
  • [5] Javad, A., Mojtaba, H., & Aria, A. (2022). A memory-based filter for long-term error denoising of MEMS-gyros. IEEE Transactions on Instrumentation and Measurement, 71, 750-3308. https://doi.org/10.1109/TIM.2022.3178964
  • [6] Di, W., Xiaosu, X., Tao, Z., & Yongyun, Z. (2019). An EMD-MRLS de-noising method for fiber optic gyro Signal. Optik, 183, 971-987. https://doi.org/10.1016/j.ijleo.2019.03.002
  • [7] Shuwen, D., & Lujun, L. (2020). Fiber optic gyro noise reduction based on hybrid CEEMDAN-LWT method. Measurement, 161, 107-865. https://doi.org/10.1016/j.measurement.2020.107865
  • [8] Sun, T., & Liu, J. (2017). A novel noise reduction method for MEMS gyroscope. Journal of Harbin Institute of Technology, 49(9), 0367-6234. https://doi.org/10.11918/j.issn.0367-6234.201606079
  • [9] Yingjie, H., & Lu, X. (2018). An integrated approach of wavelet techniques and time series analysis in eliminating MEMS inertial gyro stochastic error. Automation and Systems, 17(20), 1834-5646. https://ieeexplore.ieee.org/document/8571935
  • [10] Zhang, N., & LiuYou, W. (2018). Signal de-noising model for MEMS gyro based on CEEM-DAN improved threshold filtering. Journal of Chinese Inertial Technology, 26(4), 1005-6734. https://doi.org/10.13695/j.cnki.12-1222/o3.2018.05.018
  • [11] Tao, L., Aigong, X., & Xin, S. (2018). EEMD interval threshold de-noising method for Inertial navigation. Acta Geodaetica et Cartographica Sinica, 7, 907-915. https://doi.org/10.11947/j.AGCS.2018.20170391
  • [12] Lang, X., Desuo, C., Wei, S., & Huaizhi, S. (2021). Denoising method for fiber optic gyro measurement signal of face slab deflection of concrete face rockfill dam based on sparrow search algorithm and variational modal decomposition. Sensors and Actuators A: Physical, 331, 112-913. https://doi.org/10.1016/j.sna.2021.112913
  • [13] Jian, L., Lixin, W., & Wenhua, L. (2022). MEMS gyro noise reduction method based on CEEM-DAN multi-scale entropy. Journal of Beijing University of Aeronautics and Astronautics, 1001-5965. https://doi.org/10.13700/j.bh.1001-5965.2021.0745
  • [14] Bingbo, C., & Xiyuan, C. (2015). Improved hybrid filter for fiber optic gyroscope signal denoising based on EMD and forward linear prediction. Sensors and Actuators A: Physical, 230, 150-155. https://doi.org/10.1016/j.sna.2015.04.021
  • [15] Mingkuan, D., Zhiyong, S., Binhan, D., Huaiguang, W., & Lanyi, H. (2021). A signal de-noising method for a MEMS gyroscope based on improved VMD-WTD. Measurement Science and Technology, 32(8), 51-12. https://doi.org/10.1088/1361-6501/abfe33
  • [16] Santiago, G., & Alejandra, M. (2022). Indefinite least squares with a quadratic constraint. Journal of Mathematical Analysis and Applications, 514(1), 126-297. https://doi.org/10.1016/j.jmaa.2022.126297
  • [17] Feng, D. (2023). Least squares parameter estimation and multi-innovation least squares methods for linear fitting problems from noisy data. Journal of Computational and Applied Mathematics, 426, 115-107. https://doi.org/10.1016/j.cam.2023.115107
  • [18] Yaqing, L., & Yong, M. (2020). Daily activity feature selection in smart homes based on Pearson correlation coefficient. Neural Processing Letters, 51, 1771-1787. https://doi.org/10.1007/s11063-019-10185-8
  • [19] Degang, W., & Dongling, L. (2022). Fuzzy comprehensive evaluation of maritime boundary delimitation schemes based on AHP-entropy weight method. Mathematical Problems in Engineering, 13, 275-7779. https://doi.org/10.1155/2022/2757779
  • [20] Kunshan, Y., & Jun, S. (2023). An information entropy-based grey wolf optimizer. Soft Computing, 27, 4669-4684. https://doi.org/10.1007/s00500-022-07593-9
  • [21] Yan, G., Wang, J., & Zhou, X., High-precision simulator for strapdown inertial navigation systems based on real dynamics. Journal of Navigation and Positioning, 10, 10-96. https://doi.org/10.16547/j.cnki.10-1096.20150406
  • [22] Yi, G., Donghang, L., & Piao, G. (2021). Aircraft attitude calculation based on gradient descent algorithm. Electronic Design Engineering, 23, 0007-04. https://doi.org/10.14022/j.issn1674-6236.2021.23.002
Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.baztech-3fc1c6d6-2f90-4801-8e2c-01e195c6092e
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