An adaptive extended Kalman filter with application to compartment models

Ozbek L., Efe M.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, vol.33, no.1, pp.145-158, 2004 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 33 Issue: 1
  • Publication Date: 2004
  • Doi Number: 10.1081/sac-120028438
  • Journal Name: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.145-158
  • Keywords: nonlinear estimation, extended Kalman filter, compartment models, OBSERVER
  • Ankara University Affiliated: Yes

Abstract

In this paper the ingestion and subsequent metabolism of a drug in a given individual, are investigated through the use of compartmental models. An adaptive formulation of the widely used extended Kalman filter (EKF) has been derived in order to solve the resulting nonlinear estimation problem at the output of the compartments. The adaptive EKF employs a forgetting factor to emphasize artificially the effect of current data by exponentially weighting the observations. The dependence of EKF's performance on the selection of appropriate values of the arbitrary matrices, the measurement covariance R and the process noise covariance Q has been demonstrated through simulations. With the appropriate choice of the matrices the EKF provides a very useful tool for online estimation of both the state and the parameters.