Kalman Filtering and Forecasting Algorithms with Use of Nonparametric Functional Estimators

  • Gennady KoshkinEmail author
  • Valery Smagin
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 175)


The paper deals with the Kalman filtering and forecasting algorithms for a class of time-varying systems with unknown additive inputs. Such classes include object models with possible failures and also with models of controlled processes with unknown disturbances. The designed algorithms are based on combining the Kalman filter and nonparametric estimator. Examples are given to illustrate the usefulness of the proposed approach.


Kalman filtering and forecasting Unknown disturbances Nonparametric estimator 



Work supported by Russian Foundation for Basic Research (projects 13-08-00744, 13-08-01015), Program for Improving the Competitiveness of TSU (Tomsk State University) among the Worlds Leading Scientific, and Laboratory of Geological Informatics of Computer Science Department of TSU.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.National Research Tomsk State UniversityTomskRussia

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