Multiple Model Optimization-Based Estimators Using Horizon Scenario Tree

In this paper, a multiple model (MM) framework using horizon scenario tree (RST) is proposed for optimization- based state estimators. The problem is addressed by structuring a sliding window (SW) of measurement history as a bounded tree of scenarios. The scenario with minimum measurement residual is then used to advance the sliding window optimizer. A benchmark multi-model estimation target tracking problem having constant velocity and constant turn rate models is used in the study to evaluate the proposed architecture. Compared to the interacting multiple model (IMM) filter solution, Monte Carlo's numerical results validate the ability of the proposed algorithm to efficiently identify the dynamic changes while producing estimates that switch the model to achieve accurate tracking performance.