AN ORGANIZING PRINCIPLE FOR DYNAMIC ESTIMATION

This paper develops a general multicriteria framework for the sequential estimation of process states. Three well-known state estimation algorithms (the Viterbi, Larson-Peschon, and Kalman filters) are derived as monocriterion specializations. The multicriteria estimation framework is used to clarify both Bayesian and classical statistical procedures for treating potential model misspecification. A recently developed bicriteria specialization (flexible least cost), explicitly designed to take specification errors into account, is also reviewed. The latter application suggests how the multicriteria framework might be used to construct estimation algorithms capable of handling disparate sources of information coherently and systematically, without forced scalarization. © 1990 Plenum Publishing Corporation.