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Adaptive two-stage Kalman filter in the presence of unknown random bias

Cited 54 time in Web of Science Cited 69 time in Scopus
Authors
Kim, Kwang Hoon; Lee, Jang Gyu; Park, Chan Gook
Issue Date
2006-09
Publisher
Wiley-Blackwell
Citation
International Journal of Adaptive Control and Signal Processing 2006; 20:305–319
Keywords
two-stage Kalman filteradaptive Kalman filtercovariance rescalingunknown input
Abstract
The well-known conventional Kalman filter gives the optimal solution but requires an accurate system
model and exact stochastic information. In a number of practical situations, the system model has
unknown bias and the Kalman filter with unknown bias may be degraded or even diverged. The two-stage
Kalman filter (TKF) to consider this problem has been receiving considerable attention for a long time.
Until now, the optimal TKF for system with a constant bias or a random bias has been proposed by several
researchers. In case of a random bias, the optimal TKF assumes that the information of a random bias is
known. But the information of a random bias is unknown or incorrect in general. To solve this problem,
this paper proposes two adaptive filters, such as an adaptive fading Kalman filter (AFKF) and an adaptive
two-stage Kalman filter (ATKF). Firstly, the AFKF is designed by using the forgetting factor obtained
from the innovation information and the stability of the AFKF is analysed. Secondly, the ATKF to
estimate unknown random bias is designed by using the AFKF and the performance of the ATKF is
verified by simulation.
ISSN
0890-6327
Language
English
URI
http://hdl.handle.net/10371/69253
DOI
https://doi.org/10.1002/acs.900
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Mechanical Aerospace Engineering (기계항공공학부)Journal Papers (저널논문_기계항공공학부)
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