Performance prediction of the interacting multiple model algorithm

The Interacting Multiple Model (IMM) algorithm has been shown to be one of the most cost-effective hybrid state estimation schemes. Its performance, however, could only be evaluated via expensive Monte-Carlo simulations. An effective approach to the performance evaluation without recourse to simulations is presented in this paper. This approach is based on performance measure of hybrid nature in the sense that it is a continuous-valued matrix function of a discrete-valued sequence - the system mode sequence. This system mode sequence is an essential description of the scenario of the problem on which the performance of the algorithm is dependent and being predicted. The performance measure is efficiently calculated in an off-line recursion. The capability of this approach in predicting quantitatively the average performance of the algorithm is illustrated via two important examples: a generic Air Traffic Control tracking problem and a nonstationary noise identification problem.