Best Model Augmentation for Variable-Structure Multiple-Model Estimation

Abstract

A new approach, referred to as best model augmentation (BMA), for variable-structure multiple-model (VSMM) estimation is presented. Here the original set of models is augmented by a variable set of models intended to best match the unknown true mode. Based on the Kullback-Leiber (KL) information, two versions of the criterion serving as a metric of the closeness between candidate models and the true mode are derived in the space of states and measurements, respectively. The model set adaptation (MSA) in BMA turns out to be an online optimization problem based on the KL criterion, which can be solved easily. The performance of the proposed BMA approach is evaluated via several scenarios for maneuvering target tracking. Simulation results demonstrated the effectiveness of BMA compared with the interacting multiple-model (IMM) algorithm and the expected-mode augmentation (EMA) algorithm.