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Asymptotically Optimal Discrete Time Nonlinear Filters From Stochastically Convergent State Process Approximations
"... We consider the problem of approximating optimal in the MMSE sense nonlinear filters in a discrete time setting, exploiting properties of stochastically convergent state process approximations. More specifically, we consider a class of nonlinear, partially observable stochastic systems, comprised b ..."
Abstract

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We consider the problem of approximating optimal in the MMSE sense nonlinear filters in a discrete time setting, exploiting properties of stochastically convergent state process approximations. More specifically, we consider a class of nonlinear, partially observable stochastic systems, comprised by a (possibly nonstationary) hidden stochastic process (the state), observed through another conditionally Gaussian stochastic process (the observations). Under general assumptions, we show that, given an approximating process which, for each time step, is stochastically convergent to the state process in some appropriate sense, an approximate filtering operator can be defined, which converges to the true optimal nonlinear filter of the state in a strong and well defined sense, i.e., compactly in time and uniformly in a completely characterized measurable set of probability measure almost unity, also providing a purely quantitative justification of Egoroffâ€™s Theorem for the problem at hand. The results presented in this paper can form a common basis for the analysis and characterization of a number of heuristic approaches for approximating a large class of optimal nonlinear filters, such as approximate grid based techniques, known to perform well in a variety of applications.