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Nonlinear Estimation Methods: Polynomial Systems Approach

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Nonlinear Industrial Control Systems

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Abstract

Attention now turns to nonlinear filtering problems that are related to some of the minimum variance control laws discussed previously. Two forms of the estimator are described that have a nonlinear minimum variance form. The first has the virtue of simplicity and the second is useful since it can be related to Wiener or Kalman filtering when the system is linear. They can both include nonlinear communication channel dynamics in the problem construction. This is the most useful learning point and is a feature not usually considered. The channel equalization design example illustrates the useful structure of the polynomial-based solution and the computations involved.

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Authors and Affiliations

  1. Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK

    Michael J. Grimble

  2. Industrial Systems and Control Limited, Glasgow, UK

    Paweł Majecki

Authors
  1. Michael J. Grimble
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  2. Paweł Majecki
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Correspondence to Michael J. Grimble .

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Grimble, M.J., Majecki, P. (2020). Nonlinear Estimation Methods: Polynomial Systems Approach. In: Nonlinear Industrial Control Systems. Springer, London. https://doi.org/10.1007/978-1-4471-7457-8_12

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