Hybrid grid multiple-model estimation with application to maneuvering target tracking


Estimation for discrete-time stochastic systems with parameters varying in an exceedingly continuous space is taken into account during this paper. Justified by an analysis of model approximation, a unique approach, referred to as hybrid grid multiple model (HGMM), is proposed for state estimation. The model set employed by HGMM may be a combination of a fastened coarse grid and an adaptive fine grid to hide the mode space with a relatively small variety of models. Next, 2 fundamental issues of the HGMM approach-model-set sequence-conditioned estimation and style of adaptive fine models-are addressed. Then, primarily based on two model-set designs by moment matching, HGMM estimation algorithms are presented. Finally, performance of the developed HGMM estimation algorithms is evaluated on benchmark tracking scenarios, and simulation results demonstrate their superiority to the state-of-the-art MM estimation algorithms in terms of accuracy and computational complexity.

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