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Telecommunications and Radio Engineering
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ISSN Imprimir: 0040-2508
ISSN En Línea: 1943-6009

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Telecommunications and Radio Engineering

DOI: 10.1615/TelecomRadEng.v78.i14.70
pages 1295-1301

AN ESTIMATION OF THE MEMS GYROSCOPE ERROR BASED ON THE KALMAN FILTER ALGORITHM

H. Zhang
Qingdao University, 308 Ningxia Road, Qingdao, Shangdong, P.R. of China, 266071
W. Wang
Qingdao University, 308 Ningxia Road, Qingdao, Shangdong, P.R. of China, 266071
W. Li
Qingdao University, 308 Ningxia Road, Qingdao, Shangdong, P.R. of China, 266071
P. Wang
Qingdao University, 308 Ningxia Road, Qingdao, Shangdong, P.R. of China, 266071
G. Ren
Qingdao University, 308 Ningxia Road, Qingdao, Shangdong, P.R. of China, 266071
G. Ma
Qingdao University, 308 Ningxia Road, Qingdao, Shangdong, P.R. of China, 266071
S. Wang
Qingdao University, 308 Ningxia Road, Qingdao, Shangdong, P.R. of China, 266071
Yu. Zheng
Qingdao University, 308 Ningxia Road, Qingdao, Shangdong, P.R. of China, 266071
S. N. Shulga
V. Karazin National University of Kharkiv, 4 Svobody Sq., Kharkiv 61022, Ukraine

SINOPSIS

MEMS inertial devices are more prominent on the size and cost advantages, but it is much to be desired at the aspect of resolution and accuracy. At present, it is unable to meet the precision requirements of the inertial posture measurement system. The research environment of this paper is the angular rate measurement system composed of MSMS gyroscopes and its sampling circuit. Under the analysis of multiple error components, the measurement errors of MEMS gyroscopes are corrected and compensated by using the Kalman filter algorithm. These errors are the zero-drift error, the scale-factor error, a non-linear square sensitive error, and acceleration-sensitive error. Experimental results show that the various error factors of MEMS gyroscope have a relatively large effect on small angular rate but their effects can be ignored for running time at high angular rate. Thus, the gyroscope working in small angular rate must be revised in accordance with the results of calibration curve.

REFERENCIAS

  1. Zhou Xiaoyao, Zhng Zhiyong, Fan Dapeng, et al., (2011) Improved Angular Velocity Estimation Using MEMS Sensors with Applications in Miniature Inertial Stabilized Platforms, Chinese Journal of Aeronautics, 24(5), pp. 648-656.

  2. Liu Xiaowei, Weng Rui, Li Hai, Zhang Haifeng, et al., (2017) Ball-disk rotor gyroscope adaptive quick-start technique, Frontiers of Information Technology & Electronic Engineering, 18(9), pp. 1430-1436.

  3. Zega, V., Comi, C., Minotti, P., Langfelder, G. et al., (2018) A new MEMS three-axial frequency-modulated (FM) gyroscope: a mechanical perspective, European Journal of Mechanics/A Solids, 70(3), pp. 203-212.

  4. Nie Yongfang, Zhang Tao, et al., (2018) Scaling parameters selection principle for the scaled unscented Kalman filter, Journal of Systems Engineering and Electronics, 29(3), pp. 601-610.

  5. Qian Huaming, Liu Ke, Jiao Zhibo, Ma Junda, et al., (2017) Blind adaptive constrained constant modulus algorithms based on unscented Kalman filter for beam forming, Journal of Central South University, 24(10), pp. 2342-2352.

  6. Wang Hongwei, Zhang Wei, Zuo Junyi, Wang Heping, et al., (2017) Generalized cubature quadrature Kalman filters derivations and extensions, Journal of Systems Engineering and Electronics, 28(3), pp. 556-562.

  7. Wu Hao, Chen Shuxin, and Yang Binfeng, (2016) Robust range-parameterized cubature Kalman filter for bearings-only tracking, Journal of Central South University, 23(6), pp. 1399-1405.

  8. Mo Mingqi, Li Yuxuan, Xu Jiaxuan, et al., (2016) Development of an adaptive Kalman filter-based storm tide forecasting model, Journal of Hydrodynamics, 28(6), pp. 1029-1036.

  9. Tang Youmin, Jaison Ambandan, Chen Dake, et al., (2014) Nonlinear Measurement Function in the Ensemble Kalman Filter, Advances in Atmospheric Sciences, 31(3), pp. 551-558.

  10. Jiang Lihe , Chen Zhiwei, Ren Zhongxin, Liu Jianqiang, Bin Hasituya, et al., (2017) Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation, Journal of Integrative Agriculture, 16(10), pp. 2283-2299.

  11. Liu Changyun, Penglang Shui, Li Song, et al., (2011) Unscented extended Kalman filter for target tracking, Journal of Systems Engineering and Electronics, 22(2), pp. 188-192.

  12. Yang Hai, Li Wei, Luo Chengming, et al., (2015) Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method, Journal of Central South University, 4, pp. 1324-1333.


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