Journal
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
Volume 52, Issue 1, Pages 122-136Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAES.2015.140423
Keywords
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Funding
- National 973 Project of China [2013CB329405]
- NASA/LEQSF-Phase3-06 [NNX13AD29A]
- Natural Science Foundation of China [61403309, 61135001, 61174138]
- Aerospace Support Technology Foundation of China [2014-HT-XGD]
- Natural Science Basic Research Plan in Shaanxi Province of China [2015JQ6215]
- Fundamental Research Funds for the Central Universities of China [3102014KYJD030]
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Estimation for discrete-time stochastic systems with parameters varying in a continuous space is considered in this paper. Justified by an analysis of model approximation, a novel approach, called hybrid grid multiple model (HGMM), is proposed for state estimation. The model set used by HGMM is a combination of a fixed coarse grid and an adaptive fine grid to cover the mode space with a relatively small number of models. Next, two fundamental problems of the HGMM approach-model-set sequence-conditioned estimation and design of adaptive fine models-are addressed. Then, 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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