An adaptive UKF filtering algorithm for self alignment in the swaying base of SINS

DOI

10.2991/nceece-15.2016.238How to use a DOI?

Keywords

UKF; SINS; Initial Alignment;Kalman; Adaptive Filter

Abstract

To the nonlinear model errors caused by the swaying base, unscented Kalman filters and Adaptive Unscented Kalman Filter (AUKF) are designed (UKF) for fine alignment respectively. In this paper, the adaptive estimation principle is introduced into the UKF algorithm. AUKF algorithm can balance automatically the right of the state information and observation information in the filtering result, so that to real-time adjust the covariance of the state vector and observation vector. The experimental results show: Compared with normal UKF algorithm, the adaptive UKF algorithm can eliminate the appearance of abnormal error and improve the accuracy and reliability of self alignment in the swaying base of SINS. In the following three direction east, north and day, the angle accuracy of misalignment is increased by 0.2 ', 0.2 ' and 5.0 '; convergence time is shortened by 10 s, 28s and 27s 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/).

Download article (PDF)

TY  - CONF
AU  - Wan-xin Su
PY  - 2015/12
DA  - 2015/12
TI  - An adaptive UKF filtering algorithm for self alignment in the swaying base of SINS
BT  - Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 1350
EP  - 1356
SN  - 2352-5401
UR  - https://doi.org/10.2991/nceece-15.2016.238
DO  - 10.2991/nceece-15.2016.238
ID  - Su2015/12
ER  -