Design and evaluation of a model-group switching algorithm for multiple-model estimation with variable structure
X. Rong Li, Youmin M. Zhang, Xiaorong Zhi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
A variable-structure multiple-model (VSMM) estimator, called model- group switching (MGS) algorithm, has been developed recently. It is the first VSMM estimator that is generally applicable to a large class of problem with hybrid (continuous and discrete) uncertainties. In this algorithm, the model set is made adaptive by switching among a number of predetermined groups of models. It has the potential to be substantially superior to fixed-structure MM estimators, including the interacting multiple-model (IMM) estimator. Many issues in the application of this algorithm are investigated, such as the model-group activation logic and model- group design, via a detailed design for a problem of tracking a maneuvering target using a time-varying set of models, each characterized by a representative value of the target's expected acceleration. Simulation results are given to demonstrate the performance (based on reasonable and complete measures) and computational complexity of the MGS algorithm, relative to the IMM estimators, under carefully designed random and deterministic scenarios.