Acta Geodaetica et Cartographica Sinica >

2018 , Vol. 47 >Issue 4: 480 - 489

DOI: https://doi.org/10.11947/j.AGCS.2018.20170098

Total Kalman Filter Method of Dynamic EIV Model

  • YU Hang ,
  • WANG Jian ,
  • WANG Leyang ,
  • NING Yipeng ,
  • LIU Zhiping
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  • 1. School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China;
    2. School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
    3. Faculty of Geomatics, East China University of Technology, Nanchang 330013, China

Received date: 2017-02-28

  Revised date: 2017-12-29

  Online published: 2018-05-02

Supported by

The National Key Research and Development Program of China (No. 2016YFC0803103);The National Natural Science Foundation of China (No. 41664001);The Support Program for Outstanding Youth Talent in Jiangxi Province (No. 20162BCB23050)

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Abstract

For the case of the adjustment method of dynamic errors-in-variables(EIV)model ignoring the random errors in the state propagating matrix of system equations,this paper establishes a dynamic EIV model which considers the errors of each elements in both observation equations and system equations.A total Kalman filter method (TKF) and its approximated precision estimator are proposed based on this dynamic EIV model.The similarities and differences of the proposed method,the existing total Kalman filter methods and total least squares (TLS) methods are also analyzed.The results show that the proposed method is statistically superior to the standard Kalman filter method and the existing total Kalman filter methods.

Key words： total least squares; total Kalman filter; dynamic EIV model; prior information; virtual observations

Cite this article

YU Hang , WANG Jian , WANG Leyang , NING Yipeng , LIU Zhiping . Total Kalman Filter Method of Dynamic EIV Model[J]. Acta Geodaetica et Cartographica Sinica, 2018 , 47(4) : 480 -489 . DOI: 10.11947/j.AGCS.2018.20170098

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