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

Random vector space approach applied in integrated navigation information fusion of UAVs

Authors
Rongjun Mu, Yuntian Li, Yongzhi Shan
Corresponding Author
Rongjun Mu
Available Online November 2016.
DOI
https://doi.org/10.2991/aest-16.2016.11How to use a DOI?
Keywords
random vector space; optimal estimation; data fusion; integrated navigation.
Abstract
Federated Kalman Filter (FKF), is the most widely used distributed data fusion algorithm. Whilst FKF required local systems of the same system model, which is difficult to satisfy in most circumstances. How to balance the estimation accuracy and the calculating load is an urgent problem needs to be solved. Random Vector Space treats state predictions and estimations of both local and global modules as RVS bases equally. Then the state optimal estimation can be denoted through the combination of these bases. Replacing the time-updating of global module in FKF with RVS approach draw a higher level of accuracy with the same calculating time. Simulation results indicate the position and velocity estimation accuracy of three axes are improved by 1.59%, 1.53%, 1.29% and 19.9%, 13.3%, 17.6%, respectively.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-257-2
ISSN
1951-6851
DOI
https://doi.org/10.2991/aest-16.2016.11How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Rongjun Mu
AU  - Yuntian Li
AU  - Yongzhi Shan
PY  - 2016/11
DA  - 2016/11
TI  - Random vector space approach applied in integrated navigation information fusion of UAVs
BT  - 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
PB  - Atlantis Press
SP  - 86
EP  - 92
SN  - 1951-6851
UR  - https://doi.org/10.2991/aest-16.2016.11
DO  - https://doi.org/10.2991/aest-16.2016.11
ID  - Mu2016/11
ER  -