Application of adaptive Sage-Husa and AUKF filtering algorithm In Initial Alignment of SINS

DOI

10.2991/icmmcce-15.2015.373How to use a DOI?

Keywords

Kalman; Sage-Husa; AUKF ;SINS; initial alignment; filter

Abstract

When the system model and noise statistical characteristics are known, the conventional Kalman filtering algorithm is suitable. When the noise statistics are unknown and large initial misalignment angles results in system nonlinear, the application of liner error model and Kalman filtering algorithm is very been subjected to restriction. This text, two kinds of adaptive Sage-Husa and adaptive UKF filtering algorithm be simplified respectively.In adaptive Sage-Husa filtering algorithm, automatic on-line estimation and correction for the noise parameters, the state of the system and the state estimate covariance by the observed data. The algorithm improve the convergence speed and alignment accuracy effectively. In adaptive UKF filtering algorithm, AUKF algorithm can automatically balance the weight ratio of state and observation information in filtering to adjust the covariance of state vector and observation vector in real-time, thereby improving system performance. Experimental results showed that the use of adaptive Sage-Husa and UKF filtering algorithm can obtain better alignment accuracy and capacity of resisting disturbance.

Copyright

© 2015, 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  - Wan-xin Su
PY  - 2015/12
DA  - 2015/12
TI  - Application of adaptive Sage-Husa and AUKF filtering algorithm In Initial Alignment of SINS
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.373
DO  - 10.2991/icmmcce-15.2015.373
ID  - Su2015/12
ER  -