On the minimax robust Kalman Filter: A bounded estimation resources approach

Vadim Azhmyakov, Nelson Castano, Jose Perea Arango, Piotr Graczyk, Fabio Humberto Sepulveda Murillo

Resultado de la investigación: Contribución a una conferenciaPaper

Resumen

© 2017 IEEE. This paper is devoted to a generalization of the non-standard Kalman Filter (KF) introduced in [4]. We deal with some restrictions of the technical resources in the context of a state estimation problem and study a constrained convex program. Moreover, we replace two main concepts of the conventional KF, namely, the fundamental Normality Hypothesis (NH) and the unconstrained optimization approach. The minimax methodology we propose make it possible to develop an effective quasi-explicit solution method for the practically motivated generalization of the Kalman-type filter. We present a rigorous formal analysis of the obtained algorithm. The resulting non-linear filter possesses a strong optimality properties.
Idioma originalInglés estadounidense
Páginas1-6
Número de páginas6
DOI
EstadoPublicada - 30 ene 2018
Evento2017 IEEE 3rd Colombian Conference on Automatic Control, CCAC 2017 - Conference Proceedings -
Duración: 30 ene 2018 → …

Conferencia

Conferencia2017 IEEE 3rd Colombian Conference on Automatic Control, CCAC 2017 - Conference Proceedings
Período30/01/18 → …

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    Azhmyakov, V., Castano, N., Arango, J. P., Graczyk, P., & Murillo, F. H. S. (2018). On the minimax robust Kalman Filter: A bounded estimation resources approach. 1-6. Papel presentado en 2017 IEEE 3rd Colombian Conference on Automatic Control, CCAC 2017 - Conference Proceedings, . https://doi.org/10.1109/CCAC.2017.8276395