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Studia Geophysica et Geodaetica

, Volume 61, Issue 1, pp 19–34 | Cite as

A general weighted total Kalman filter algorithm with numerical evaluation

  • Vahid Mahboub
  • Mohammad SaadatsereshtEmail author
  • Alireza A. Ardalan
Article

Abstract

An applicable algorithm for Total Kalman Filter (TKF) approach is proposed. Meanwhile, we extend it to the case in which we can consider arbitrary weight matrixes for the observation vector, the random design matrix and possible correlation between them. Also the updated dispersion matrix of the predicted unknown is given. This approach makes use of condition equations and straightforward variance propagation rules. It is applicable to data fusion within a dynamic errors-in-variables (DEIV) model, which usually appears in the determination of the position and attitude of mobile sensors. Then, we apply for the first time the TKF algorithm and its extended version named WTKF to a DEIV model and compare the results. The results show the efficiency of the proposed WTKF algorithm. In particular in the case of large weights, WTKF shows approximately 25% improvement in contrast to TKF approach.

Keywords

Total Kalman filter dynamic errors-in-variables model prediction weight matrix mobile mapping kinematic positioning 

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Copyright information

© Institute of Geophysics of the ASCR, v.v.i 2017

Authors and Affiliations

  • Vahid Mahboub
    • 1
  • Mohammad Saadatseresht
    • 1
    Email author
  • Alireza A. Ardalan
    • 1
  1. 1.School of Surveying and Geospatial Engineering, College of EngineeringUniversity of TehranTehranIran

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