TY - GEN
T1 - Development of a fuzzy logic-based adaptive Kalman filter
AU - Escamilla-Ambrosio, P. J.
AU - Mort, N.
N1 - Publisher Copyright:
© 2001 EUCA.
PY - 2001
Y1 - 2001
N2 - In this paper, after reviewing the traditional Kalman filter formulation, a development of a fuzzy logic-based adaptive Kalman filter is outlined. The adaptation is in the sense of adaptively tuning, on-line, the measurement noise covariance matrix R or the process noise covariance matrix Q. This improves the Kalman filter performance and prevents filter divergence when R or Q are uncertain. Based on the whiteness of the filter innovation sequence and employing the covariance-matching technique the tuning process is carried out by a fuzzy inference system. If a statistical analysis of the innovation sequence shows discrepancies with its expected statistics then a fuzzy inference system adjusts a factor through which the matrices R or Q are tuned on line. This fuzzy logic-based adaptive Kalman filter is tested on a numerical example. The results are compared with these obtained using a conventional Kalman filter and a traditionally adapted Kalman filter. The fuzzy logic-based adaptive Kalman filter showed better results than its traditional counterparts.
AB - In this paper, after reviewing the traditional Kalman filter formulation, a development of a fuzzy logic-based adaptive Kalman filter is outlined. The adaptation is in the sense of adaptively tuning, on-line, the measurement noise covariance matrix R or the process noise covariance matrix Q. This improves the Kalman filter performance and prevents filter divergence when R or Q are uncertain. Based on the whiteness of the filter innovation sequence and employing the covariance-matching technique the tuning process is carried out by a fuzzy inference system. If a statistical analysis of the innovation sequence shows discrepancies with its expected statistics then a fuzzy inference system adjusts a factor through which the matrices R or Q are tuned on line. This fuzzy logic-based adaptive Kalman filter is tested on a numerical example. The results are compared with these obtained using a conventional Kalman filter and a traditionally adapted Kalman filter. The fuzzy logic-based adaptive Kalman filter showed better results than its traditional counterparts.
KW - Adaptive Kalman filtering
KW - covariance matching technique
KW - filter divergence
KW - fuzzy logic
KW - innovation sequence
UR - http://www.scopus.com/inward/record.url?scp=84947461677&partnerID=8YFLogxK
U2 - 10.23919/ecc.2001.7076177
DO - 10.23919/ecc.2001.7076177
M3 - Contribución a la conferencia
AN - SCOPUS:84947461677
T3 - 2001 European Control Conference, ECC 2001
SP - 1768
EP - 1773
BT - 2001 European Control Conference, ECC 2001
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th European Control Conference, ECC 2001
Y2 - 4 September 2001 through 7 September 2001
ER -