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This paper proposes a Gaussian sum filtering (GSF) framework for the state estimation of Markov jump non-linear systems. Through presenting the Gaussian sum approximations about the model-conditioned state posterior probability density functions, a general GSF framework in the minimum mean square error sense is derived. The minor Gaussian-set design is utilised to merge the Gaussian components at...
A new algorithm for the reduction of blocking artifacts in images compressed using block-based discrete cosine transform (DCT) is proposed in this paper. Firstly, a Bayesian model and the Markov network assumption are adopted for our deblocking algorithm. An input blocking image is divided into observation nodes of the network. Then a simplified method is applied to find approximate optimal solutions...
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...
This paper considers the problem of state estimation for a hybrid system with Markovian switching parameters which belong to a continuous space. A hybrid grid multiple model (HGMM) estimator is presented. The total model set for HGMM is the combination of a fixed coarse grid and an adaptive fine grid. Three practical algorithms in this scheme are developed. These algorithms are used for state estimation...
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