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Volume 29 Issue 1
 
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Article Navigation >  Journal of Unmanned Undersea Systems  > 2021  >  29(1): 054-59
ZHAO Jun-bo, GE Xi-yun, CHENG Yue, LI Jin. SINS/DVL/LBL-integrated Navigation Technology Based on Improved Variational Bayesian Filtering[J]. Journal of Unmanned Undersea Systems, 2021, 29(1): 054-59. doi: 10.11993/j.issn.2096-3920.2021.01.008
Citation: ZHAO Jun-bo, GE Xi-yun, CHENG Yue, LI Jin. SINS/DVL/LBL-integrated Navigation Technology Based on Improved Variational Bayesian Filtering[J]. Journal of Unmanned Undersea Systems, 2021, 29(1): 054-59. doi: 10.11993/j.issn.2096-3920.2021.01.008
shu

SINS/DVL/LBL-integrated Navigation Technology Based on Improved Variational Bayesian Filtering

doi: 10.11993/j.issn.2096-3920.2021.01.008

  • China Ship Scientific Research Center, Wuxi 214082, China

  • Received Date: 2020-04-20
  • Rev Recd Date: 2020-05-23
  • Publish Date: 2021-03-01
    Abstract
  • To solve the integrated navigation problem of an undersea vehicle strapdown inertial navigation system (SINS), Doppler velocity log(DVL), and long baseline(LBL) location equipment, a centralized filtering scheme is proposed in this study and a SINS/DVL/LBL-integrated navigation model is established. During integrated navigation, using the classical Kalman filtering method leads to the problem in which the filtering accuracy is reduced due to time-varying measurement noise variance and the interference of outliers. Accordingly, an improved variational Bayesian filtering method is proposed by combining a variational Bayesian filter with an IGGIII weight function. Integrated navigation simulation is conducting using MATLAB software, and results show that the proposed filtering method has strong self-adaptive and anti-outlier abilities as well as a higher filtering accuracy than the classical Kalman filtering and variational Bayesian filtering methods.

     

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