Research and Application of Moving Tracking of Stewart Based on Multi-innovation EKF Algorithm

DOI

10.2991/iccsae-15.2016.90How to use a DOI?

Keywords

Extend Kalman Filter; Multi-innovation; Multi-innovation Extend Kalman Filter;Simulation analyses

Abstract

Because of the low estimation accuracy of normal extended Kalman Filter in strong nonlinear system, an improved extended Kalman Filter (MI-EKF) is presented to solve the problem, and the filtering accuracy is greatly improved. In this paper, multi-innovation theory is applied to EKF, and the multi-innovation EKF (MI-EKF) is proposed. MI-EKF has better precision and stability, because MI-EKF considers not only the current measured value, but also give full consideration to the time before state of motion. Finally, the improvement algorithm is used the moving tracking of six degree freedom stewart motion platform, the simulation results show that the improved MI-EKF algorithm is superior to the standard EKFalgorithm.

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  - Sujian Sheng
AU  - Bo Yang
AU  - Pinle Qin
AU  - Xiaoqing Chen
PY  - 2016/02
DA  - 2016/02
TI  - Research and Application of Moving Tracking of Stewart Based on Multi-innovation EKF Algorithm
BT  - Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering
PB  - Atlantis Press
SP  - 478
EP  - 485
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccsae-15.2016.90
DO  - 10.2991/iccsae-15.2016.90
ID  - Sheng2016/02
ER  -