TY - GEN
T1 - On process noise covariance estimation
AU - Nguyen, H. N.
AU - Guillemin, F.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/18
Y1 - 2017/7/18
N2 - This paper proposes a method for estimating the process noise covariance matrix, using multiple Kalman filters. The basic idea is to employ the difference between the expected prediction error covariance, calculated in the Kalman filters, and the measured prediction error covariance. The required estimate of the process noise covariance is obtained by solving a least squares problem. One simulated example is used to illustrate the main benefits of the proposed method.
AB - This paper proposes a method for estimating the process noise covariance matrix, using multiple Kalman filters. The basic idea is to employ the difference between the expected prediction error covariance, calculated in the Kalman filters, and the measured prediction error covariance. The required estimate of the process noise covariance is obtained by solving a least squares problem. One simulated example is used to illustrate the main benefits of the proposed method.
UR - https://www.scopus.com/pages/publications/85027875835
U2 - 10.1109/MED.2017.7984305
DO - 10.1109/MED.2017.7984305
M3 - Conference contribution
AN - SCOPUS:85027875835
T3 - 2017 25th Mediterranean Conference on Control and Automation, MED 2017
SP - 1345
EP - 1348
BT - 2017 25th Mediterranean Conference on Control and Automation, MED 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th Mediterranean Conference on Control and Automation, MED 2017
Y2 - 3 July 2017 through 6 July 2017
ER -