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A Comparison Study on Performance of an Adaptive Filter with Other Estimation Methods for State Estimation in High-Dimensional System

In: Advances in Statistical Methodologies and Their Application to Real Problems

Author

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  • Hong Son Hoang
  • Remy Baraille

Abstract

In this chapter, performance comparison between the adaptive filter (AF) and other estimation methods, especially with the variational method (VM), is given in the context of data assimilation problem in dynamical systems with (very) high dimension. The emphasis is put on the importance of innovation approach which is a basis for construction of the AF as well as the choice of a set of tuning parameters in the filter gain. It will be shown that the innovation representation for the initial dynamical system plays essential role in providing stability of the assimilation algorithms for stable and unstable system dynamics. Numerical experiments will be given to illustrate the performance of the AF.

Suggested Citation

  • Hong Son Hoang & Remy Baraille, 2017. "A Comparison Study on Performance of an Adaptive Filter with Other Estimation Methods for State Estimation in High-Dimensional System," Chapters, in: Tsukasa Hokimoto (ed.), Advances in Statistical Methodologies and Their Application to Real Problems, IntechOpen.
    • Handle: RePEc:ito:pchaps:107820
    DOI: 10.5772/67005
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    More about this item

    Keywords

    dynamical system; innovation process; filter stability; minimum mean square prediction error; simultaneous stochastic perturbation;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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