Multiple-model estimation with variable structure - Part IV: Design and evaluation of model-group switching algorithm

A variable-structure multiple-model (VSMM) estimator, called model-group sw itching (MGS) algorithm, has been presented in Part III, which is the first VSMM estimator that is generally applicable to a large class of problem wi th hybrid (continuous and discrete) uncertainties. In this algorithm, the m odel-set is made adaptive by switching among a number of predetermined grou ps of models. It has the potential to be substantially more cost-effective than fixed-structure MM (FSMM) estimators, including the Interacting Multip le-Model (IMM) estimator, a number of issues of major importance in the app lication of this algorithm are investigated here, including the model-group adaptation logic and model-group design. The results of this study are imp lemented via a detailed design for a problem of tracking a maneuvering targ et using a time-varying set of models, each characterized by a representati ve value of the expected acceleration of the target. Simulation results are given to demonstrate the performance (based on more reasonable and complet e measures than commonly used rms errors alone) and computational complexit y of the MGS algorithm, relative to the fixed-structure (FSIMM) estimator u sing all models, under carefully designed and fair random and deterministic scenarios.