Failures of sequential Bayesian filters and the successes of shadowing filters in tracking of nonlinear deterministic and stochastic systems

Kevin Judd, Thomas Stemler

    Research output: Contribution to journalArticle

    15 Citations (Scopus)


    Sequential Bayesian filters, such as particle filters, are often presented as an ideal means of tracking the state of nonlinear systems. Here shadowing filters are demonstrated to perform better than sequential filters at tracking under specific circumstances. The success of shadowing filters is attributed to avoiding both well-known deficiencies of particle filters, and some newly identified problems.
    Original languageEnglish
    Pages (from-to)Article number 066206, 6pp
    JournalPhysical Review E
    Issue number6
    Publication statusPublished - 2009


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