Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)

The application analysis of adaptive filter based on polynomial AR model in GPS dynamic positioning

Authors
Xianglei Wang, Guitao Fu
Corresponding Author
Xianglei Wang
Available Online November 2016.
DOI
10.2991/aest-16.2016.4How to use a DOI?
Keywords
GPS dynamic; AR model; kalman filter; colored noise; adaptive filter.
Abstract

When modeling for colored noise, order number and the coefficient of determination of the model is a difficult problem, real-time computing applicability is very good, although but increased filtering calculation burden. Therefore based on the polynomial approximation theory, this paper proposes a polynomial AR model to colored noise modeling, model coefficient and the order number and the polynomial order, only speed up the computing speed of the filter. Through an example for a GPS cycle slip detection effectiveness of the proposed algorithm is verified.

Copyright
© 2016, 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 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
Series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
10.2991/aest-16.2016.4
ISSN
1951-6851
DOI
10.2991/aest-16.2016.4How to use a DOI?
Copyright
© 2016, 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  - Xianglei Wang
AU  - Guitao Fu
PY  - 2016/11
DA  - 2016/11
TI  - The application analysis of adaptive filter based on polynomial AR model in GPS dynamic positioning
BT  - Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
PB  - Atlantis Press
SP  - 35
EP  - 42
SN  - 1951-6851
UR  - https://doi.org/10.2991/aest-16.2016.4
DO  - 10.2991/aest-16.2016.4
ID  - Wang2016/11
ER  -