Convergence of continuous-time partitioned adaptive state estimators

Abstract

The asymptotic behavior of Bayes optimal adaptive state estimation schemes (also called the partitioned adaptive estimation algorithms) for continuous-time linear dynamic Gauss-Markov systems with unknown parameters is investigated. The unknown system parameters are assumed to belong to a finite set. The results are developed through weak consistency of the maximum likelihood and the maximum a posteriori probability estimates of the unknown parameters.