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The Conditionally Minimax Nonlinear Filtering Method and Modern Approaches to State Estimation in Nonlinear Stochastic Systems

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Abstract

We consider, in chronological order, the main results that have defined the concept of conditionally minimax nonlinear filtering. This would let us to follow all the evolution stages of this universal method, from a particular application, through basic mathematical concepts, to an advanced theory able to solve a wide class of robust estimation problems in linear and nonlinear stochastic systems.

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Authors and Affiliations

  1. Institute of Informatics Problems of Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, Moscow, Russia

    A. V. Borisov, A. V. Bosov & G. B. Miller

  2. Moscow Aviation Institute, Moscow, Russia

    A. I. Kibzun & K. V. Semenikhin

Authors
  1. A. V. Borisov
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  2. A. V. Bosov
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  3. A. I. Kibzun
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  4. G. B. Miller
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  5. K. V. Semenikhin
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Correspondence to A. V. Borisov.

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Original Russian Text © A.V. Borisov, A.V. Bosov, A.I. Kibzun, G.B. Miller, K.V. Semenikhin, 2018, published in Avtomatika i Telemekhanika, 2018, No. 1, pp. 3–17.

This paper was recommended for publication by B.M. Miller, a member of the Editorial Board

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Borisov, A.V., Bosov, A.V., Kibzun, A.I. et al. The Conditionally Minimax Nonlinear Filtering Method and Modern Approaches to State Estimation in Nonlinear Stochastic Systems. Autom Remote Control 79, 1–11 (2018). https://doi.org/10.1134/S0005117918010010

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