Publication | Closed Access
Approximation of Nonlinear Filters for Markov Systems with Delayed Observations
11
Citations
13
References
2006
Year
State EstimationStochastic Hybrid SystemTime Delay SystemNonlinear FilteringMarkov SystemEngineeringFiltering TechniqueHidden Markov ModelMarkov KernelSystems EngineeringMarkov SystemsDigital FilterStochastic AnalysisProbability TheoryMarkov Jump ProcessSignal Processing
The aim of this paper is to give some approximation results for a class of nonlinear filtering problems with delay in the observation. First, we point out some general results on the approximation problem for the filter in nonlinear filtering. In particular, we give a general procedure for obtaining some upper bounds for the different approximations we consider. This procedure is then applied in the case of nonlinear filtering problems with delay $(X,Y)$, which can be represented by means of a Markov system $(X,\hat{Y})$, in the sense that $Y_{t}=\hat{Y}_{a(t)}$. Finally, these upper bounds are computed explicitly in the particular case of Markov jump process with counting observations.
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