Model-set design, choice, and comparison for multiple-model estimation
X. Rong Li, Chen He · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
This paper deals with the design, choice, and comparison of model sets in the multiple-model (MM) approach to adaptive estimation. Most representative problems of model-set choice and design are considered. As the basis of model-set choice and design, criteria for model-set comparison and choice based on base-state estimation, mode estimation, mode identification, hybrid-state estimation, and hypothesis testing are presented first. Several computationally efficient and easily implementable solutions of the model- set choice problems based on sequential hypothesis tests are presented. Some of these solutions are optimal. Their effectiveness is verified via simulation. How these criteria and result can be used for model-set design is demonstrated via several examples. It is also demonstrated how a probabilistic model of possible scenarios can be constructed.