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 estimation schemes for hybrid systems. Its performance, however, could only be evaluated via expensive Monte Carlo simulations. An effective hybrid approach to the performance evaluation without recourse to simulations is presented. This approach is based on a scenario-conditional performance measure of hybrid nature in the sense that it is a continuous-valued matrix function of a discrete-valued random sequence-the system mode sequence. This system mode sequence is an essential description of the scenario of the problem of interest on which the performance of the algorithm is to be predicted. The performance measure is calculated efficiently in an offline recursion. The ability of this approach to predict accurately 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