Equivalent-model augmentation for variable-structure multiple-model estimation

A variable-structure multiple-model (VSMM) approach, named equivalent-model augmentation (EqMA), is proposed. Here the model set is augmented by a variable model intended to best match the unknown true mode. To fully utilize the information provided by model sequences, this variable model depends on the true mode at the previous time. Thus different previous models correspond to different augmenting models. To make the estimation process computationally feasible, the variable model at the previous time is approximated by a so-called equivalent model (EqM) which provides the closest estimation results in the sense of minimum Kullback-Leibler divergence. The online information provided by the measurements can also be incorporated into EqM. Performance of the proposed EqMA approach is evaluated via two scenarios for maneuvering target tracking. Simulation results demonstrated the effectiveness of EqMA compared with the interacting multiple-model (IMM) algorithm and the expected-mode augmentation (EMA) algorithm.