Model-set choice for multiple-model estimation

Abstract This paper deals with the choice of model set in the multiple-model (MM) approach to adaptive estimation. Most representative problems of model-set choice commonly encountered in model-set design for MM estimation and in model-set adaptation for variable-structure MM estimation are considered. Their solutions based on sequential hypothesis tests are presented. Some of these solutions are optimal in some practical, meaningful sense. They are also extremely efficient in computation and easy to implement. Their effectiveness is verified via simulation. How these results can be used for model-set design is also demonstrated.