Prior knowledge processing for initial state of Kalman filter

The paper deals with a specification of the prior distribution of the initial state for Kalman filter. The subjective prior knowledge, used in state estimation, can be highly uncertain. In practice, incorporation of prior knowledge contributes to a good start of the filter. The present paper proposes a methodology for selection of the initial state distribution, which enables eliciting of prior knowledge from the available expert information. The proposed methodology is based on the use of the conjugate prior distribution for models, belonging to the exponential family. The normal state-space model is used for demonstrating of the methodology. The paper covers processing of the prior knowledge for state estimation, available in the form of simulated data. Practical experiments demonstrate processing of prior knowledge from the urban traffic control area, which is the main application of the research. Copyright c © 2008 John Wiley & Sons, Ltd.