Asymptotically optimal discrete-time nonlinear filters from stochastically convergent state process approximations

Dionysios S. Kalogerias, Athina P. Petropulu

Research output: Contribution to journalArticle

5 Scopus citations


We consider the problem of approximating optimal in the Minimum Mean Squared Error (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, 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. In particular, the convergence is compact in time and uniform in a completely characterized set of probability measure almost unity. The results presented in this paper can form a common basis for the analysis and characterization of a number of popular but heuristic approaches for approximating optimal nonlinear filters, such as approximate grid based techniques.

Original languageEnglish (US)
Article number7098437
Pages (from-to)3522-3536
Number of pages15
JournalIEEE Transactions on Signal Processing
Issue number13
StatePublished - Jul 1 2015

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


  • Approximate nonlinear filtering
  • hidden models
  • partially observable systems
  • stochastic processes
  • {\cal C}-weak convergence

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