Proceedings of the 3rd International Conference on Electric and Electronics

The Multi-state Model Fusion Algorithm for GNSS Navigation

Authors
Dawen Zhang
Corresponding Author
Dawen Zhang
Available Online December 2013.
DOI
10.2991/eeic-13.2013.99How to use a DOI?
Keywords
GNSS, Kalman filter, data fusion, polynomial predictive, state space model
Abstract

A novel fusion algorithm, termed as the multi-state model fusion filter (MSMF), was established for the global navigation satellite systems (GNSS) navigation. Firstly, a new state space model was presented by adopting the polynomial predictive idea and state dimension expansion. It was established without the knowledge of the original state dynamics, that is, no matter the original state propagation was known or not. Then two local estimation results were obtained based on the original model and the proposed model. Whereafter, the local results were fused to get the global estimation. The simulation results of the global positioning system (GPS) navigation verified the effectiveness of the proposed method.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 3rd International Conference on Electric and Electronics
Series
Advances in Intelligent Systems Research
Publication Date
December 2013
ISBN
10.2991/eeic-13.2013.99
ISSN
1951-6851
DOI
10.2991/eeic-13.2013.99How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Dawen Zhang
PY  - 2013/12
DA  - 2013/12
TI  - The Multi-state Model Fusion Algorithm for GNSS Navigation
BT  - Proceedings of the 3rd International Conference on Electric and Electronics
PB  - Atlantis Press
SP  - 422
EP  - 425
SN  - 1951-6851
UR  - https://doi.org/10.2991/eeic-13.2013.99
DO  - 10.2991/eeic-13.2013.99
ID  - Zhang2013/12
ER  -