The Kalman Filter as an On-Line Drift Compensator in Multicomponent Analysis Determinations

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Abstract
A method is proposed for the on-line compensation of linear drift in analytical measurements. The robustness of the compensator, which is based on the Kalman filter, has been investigated by means of a parametric study. Special emphasis is given to the choice of the initial error covariance matrix, which is an important design quantity for the Kalman filter. The presented method can be applied when there is uncertainty about the presence of drift in the measurements. Some results are compared with results obtained with the non-recursive least squares method. The excellent performance of the on-line compensator suggests a possible solution for the linear drift problem. Of some theoretical importance is the fact that the proposed solution has a strong intuitive appeal: the linear drift can be considered as “two extra components” in a spectrum or equivalently as the time varying mean of the measurement noise.