Volume 44 Issue 6
Jun.  2018
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LU Zhaoxing, FANG Jiancheng, WANG Shicheng, et al. Application of adaptive FLP filter to ring laser gyro IMU calibration[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(6): 1213-1220. doi: 10.13700/j.bh.1001-5965.2017.0462(in Chinese)
Citation: LU Zhaoxing, FANG Jiancheng, WANG Shicheng, et al. Application of adaptive FLP filter to ring laser gyro IMU calibration[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(6): 1213-1220. doi: 10.13700/j.bh.1001-5965.2017.0462(in Chinese)

Application of adaptive FLP filter to ring laser gyro IMU calibration

doi: 10.13700/j.bh.1001-5965.2017.0462
Funds:

National Natural Science Foundation of China 61421063

National Natural Science Foundation of China 61473020

National Natural Science Foundation of China 61571030

National Natural Science Foundation of China 61573040

National Natural Science Foundation of China 61722103

National Natural Science Foundation of China 61503393

National High-tech Research and Development Program of China 2015AA124001

National High-tech Research and Development Program of China 2015AA124002

International (Regional) Cooperation and Communication Project 61661136007

Basic Scientific Research YWF-17-BJ-Y-71

More Information
  • Corresponding author: FANG Jiancheng, E-mail:fangjiancheng@cqjj8.com
  • Received Date: 10 Jul 2017
  • Accepted Date: 17 Nov 2017
  • Publish Date: 20 Jun 2018
  • Ring laser gyro inertial measurement unit (IMU) encounters the problem of relatively large stochastic noise because of the strenuous dithering motion. The conventional calibration method eliminates the impact on the stochastic noise by extending the measurement time, which undoubtedly reduces the calibration efficiency. To solve the problem, the adaptive forward linear prediction (FLP) filter is adopted to suppress the stochastic noises of ring laser gyro IMU calibration data and improve the calibration accuracy of the system with small amount of calibration data. Firstly, the original data is acquired from the four-position rotation calibration experiment. Secondly, the calibration data is de-noised by the adaptive FLP filter to improve its signal noise ratio (SNR). Finally, the calibration parameters are calculated with the de-noised data. The experimental results show that the stochastic noises of calibration data are de-noised effectively by the adaptive FLP filter, the SNR of the signal is improved, and more accurate calibration parameters are acquired with small amount of calibration data, which enhances the navigation accuracy of the system.

     

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