The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients

Abstract

An important problem in filtering for linear systems with Markovian switching coefficients (dynamic multiple model systems) is the one of management of hypotheses, which is necessary to limit the computational requirements. A novel approach to hypotheses merging is presented for this problem. The novelty lies in the timing of hypotheses merging. When applied to the problem of filtering for a linear system with Markovian coefficients this yields an elegant way to derive the interacting multiple model (IMM) algorithm. Evaluation of the IMM algorithm makes it clear that it performs very well at a relatively low computational load. These results imply a significant change in the state of the art of approximate Bayesian filtering for systems with Markovian coefficients.