Volume 44 Issue 5
May  2019
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DAI Qing, SUI Lifen, TIAN Yuan, TIAN Yijun, ZENG Tian. Gaussian Mixture Filter Based on Variational Bayesian Learning Optimization and Its Application to Integrated Navigation[J]. Geomatics and Information Science of Wuhan University, 2019, 44(5): 699-705. DOI: 10.13203/j.whugis20170235
Citation: DAI Qing, SUI Lifen, TIAN Yuan, TIAN Yijun, ZENG Tian. Gaussian Mixture Filter Based on Variational Bayesian Learning Optimization and Its Application to Integrated Navigation[J]. Geomatics and Information Science of Wuhan University, 2019, 44(5): 699-705. DOI: 10.13203/j.whugis20170235
shu

Gaussian Mixture Filter Based on Variational Bayesian Learning Optimization and Its Application to Integrated Navigation

  • 1.

    Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China

Funds: 

The National Natural Science Foundation of China 41674016

The National Natural Science Foundation of China 41274016

More Information
  • Author Bio:

    DAI Qing, PhD candidate, specializes in GNSS data processing and its application. E-mail:Geo_DaiQing@hotmail.com

  • Received Date: May 04, 2018
  • Published Date: May 04, 2019
    Graphical Abstract
    • Abstract
  • We present a novel Gaussian mixture filter (GMF) improved by variational Bayesian learning. The method is mainly used in time-differenced carrier phase/strap-down Inertial Navigation System integrated navigation to deal with parameter estimation of Gaussian mixture model in non-Gaussian noise environment. By means of variational Bayesian learning theory, this algorithm accurately and efficiently estimates parameters of GMF, and further refines the stochastic model. And it improves accuracy of GMF, reduces the computational complexity, and improves the effectiveness. The experimental results illustrate that the proposed filter substantially outperforms the existing algorithm in terms of estimation accuracy, and is computationally much more efficient. It provides theoretical support for integrated navigation data fusion strategy based on GMF.
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    • References (13)
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