The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 503–512, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-503-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 503–512, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-503-2017

  27 Sep 2017

27 Sep 2017

ON A NEW FAMILY OF KALMAN FILTER ALGORITHMS FOR INTEGRATED NAVIGATION

V. Mahboub, M. Saadatseresht, and A. A. Ardalan V. Mahboub et al.
  • School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran

Keywords: Kalman filter; prediction; dynamic-errors-in-variables; navigation

Abstract. Here we present a review on a new family of Kalman filter algorithms which recently developed for integrated navigation. In particular it is useful for vision based navigation due to the type of data. Here we mainly focus on three algorithms namely weighted Total Kalman filter (WTKF), integrated Kalman filter (IKF) and constrained integrated Kalman filter (CIKF). The common characteristic of these algorithms is that they can consider the neglected random observed quantities which may appear in the dynamic model. Moreover, our approach makes use of condition equations and straightforward variance propagation rules. The WTKF algorithm can deal with problems with arbitrary weight matrixes. Both of the observation equations and system equations can be dynamic-errors-in-variables (DEIV) models in the IKF algorithms. In some problems a quadratic constraint may exist. They can be solved by CIKF algorithm. Finally, we compare four algorithms WTKF, IKF, CIKF and EKF in numerical examples.