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

DOI

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.

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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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  - Proceedings of the 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  - 10.2991/aest-16.2016.11
ID  - Mu2016/11
ER  -