Design and evaluation of a model-group switching algorithm for multiple-model estimation with variable structure

X. Rong Li; Youmin Zhang; Xiaorong Zhi

doi:10.1117/12.283966

29 October 1997 Design and evaluation of a model-group switching algorithm for multiple-model estimation with variable structure

X. Rong Li, Youmin Zhang, Xiaorong Zhi

Author Affiliations +

Proceedings Volume 3163, Signal and Data Processing of Small Targets 1997; (1997) https://doi.org/10.1117/12.283966
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

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.

Citation Download Citation

X. Rong Li, Youmin Zhang, and Xiaorong Zhi "Design and evaluation of a model-group switching algorithm for multiple-model estimation with variable structure", Proc. SPIE 3163, Signal and Data Processing of Small Targets 1997, (29 October 1997); https://doi.org/10.1117/12.283966

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