Soft Computing

, Volume 23, Issue 14, pp 5559–5570 | Cite as

State estimation of nonlinear dynamic system using novel heuristic filter based on genetic algorithm

  • Seid Miad ZandaviEmail author
  • Vera Chung
Methodologies and Application


This paper introduces a new filter for nonlinear systems state estimation. The new filter formulates the state estimation problem as a stochastic dynamic optimization problem and utilizes a new stochastic method based on genetic algorithm to find and track the best estimation. In the proposed filter, each individual is set based on stochastic selection and multiple mutations to find the best estimation at every time step. The population searches the state space dynamically in a similar scheme to the optimization algorithm. This approach is applied to estimate the state of some nonlinear dynamic systems with noisy measurement and its performance is compared with other filters. The results indicate an improved performance of heuristic filters relatives to classic versions. Comparison of the results to those of extend Kalman filter, unscented Kalman filter, particle filter and heuristic filters indicated that the proposed heuristic filter called genetic filter fulfills the essential requirements of fast and accuracy for nonlinear state estimation.


State estimation Nonlinear system Genetic algorithm Genetic filter Heuristic filter 


Compliance with ethical standards

Conflict of interest

The authors have no conflict of interest.

Ethnical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information TechnologyUniversity of SydneySydneyAustralia

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