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Volume 35 Issue 2
Feb.  2009
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Journal of Beijing University of Aeronautics and Astronautics  > 2009  >  35(2): 246-250.
Liu Yuanmin, Qin Shiyin. Novel adaptive denoising method for extreme noise based on PCNN[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(1): 108-112. (in Chinese)
Citation: Chen Diansheng, Shao Zhihao, Lei Xusheng, et al. Multiscale fyzzy-adaptive Kalman filtering methods for MEMS gyros random drift[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(2): 246-250. (in Chinese)
shu

Multiscale fyzzy-adaptive Kalman filtering methods for MEMS gyros random drift

  • 1.

    School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • 2. School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • Received Date: 20 Jul 2008
  • Publish Date: 28 Feb 2009
    Abstract
  • A new time series method was proposed to construct the random drift model for the micro electro mechanical sensor (MEMS) gyro. Based on the wavelet multi scale analysis method, the gyro random drift data was decomposed to a series of scale gyro drift data with depth of 4 using bior1.5 wavelet, each scale signal was rebuilt and then constructed the corresponding multi scale time series models to reduce the overall predict error. Moreover, an adaptive Kalman filter algorithm was proposed to improve the compensation performance for the random drift noise. The noise variance was modified by using the fuzzy adaptive system which is based on the mean and variance margin of residual sequence. The effectiveness of the proposed method was proved by a series of experiments compared with multi scale analysis with simple Kalman filter (SKF). Each random item was reduced using Allan variance analysis.

     

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