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

This paper considers the problem of state estimation for a hybrid system with Markovian switching parameters in a continuous space. We propose a hybrid grid multiple model (HGMM) estimator whose model set is a combination of a fixed coarse grid and an adaptive fine grid. We also present two modelset design methods by moment matching, and apply them to practical HGMM algorithms. Simulation results show their cost-effectiveness for state estimation in maneuvering target tracking.

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