An Adaptive Filtering Method for HRG Random Error in case of Colored Noise

DOI

10.2991/icmeit-17.2017.100How to use a DOI?

Keywords

ARMA model; orthogonal projection theorem; adaptive filtering.

Abstract

ARMA modeling and filtering methods for HRG random error are studied. Firstly, the model is constructed by analyzing the autocorrelation and partial correlation of HRG stationary random signals, the applicable model is build, and then the model order is estimated. Then, the residual parameters are obtained by calculating the residual method by long autoregressive model. Under the condition of colored noise, the ARMA model cannot be whitened by the traditional state expansion method. According to the equivalence between the orthogonal projection theorem and the linear minimum variance estimation, the adaptive kalman filter formula with the control term is deduced. The new method can directly reflect the influence of colored noise on the system, which can effectively eliminate the error and get the accurate estimation of the state value. The experimental results show that the improved method is more effective than the classical kalman filtering method in filtering out the noise.

Copyright

© 2017, 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  - Haotian Yang
AU  - Lixin Wang
AU  - Zhuo Li
PY  - 2017/05
DA  - 2017/05
TI  - An Adaptive Filtering Method for HRG Random Error in case of Colored Noise
BT  - Proceedings of the 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017)
PB  - Atlantis Press
SP  - 536
EP  - 542
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-17.2017.100
DO  - 10.2991/icmeit-17.2017.100
ID  - Yang2017/05
ER  -