Abstract
Kalman Filters (KF) are at the root of many computational solutions for autonomous systems navigation problems, besides other application domains. The basic linear formulation has been extended in several ways to cope with non-linar dynamic environments. One of the latest trend is to introduce other Computational Intelligence (CI) tools, such as Fuzzy Systems or Artificial Neural Networks inside its computational loop, in order to obtain learning and advanced adaptive properties. This paper offers a short review of current approaches. © 2009 Springer Berlin Heidelberg.
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CITATION STYLE
Graña, M., Villaverde, I., Guede, J. M. L., & Fernández, B. (2009). Review of hybridizations of kalman filters with fuzzy and neural computing for mobile robot navigation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5572 LNAI, pp. 121–128). https://doi.org/10.1007/978-3-642-02319-4_15
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