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Ellipsoidal and Gaussian Kalman Filter Model for Discrete-Time Nonlinear Systems

verfasst von
Ligang Sun, Hamza Alkhatib, Boris Kargoll, Vladik Kreinovich, Ingo Neumann
Abstract

In this paper, we propose a new technique-called Ellipsoidal and Gaussian Kalman filter-for state estimation of discrete-time nonlinear systems in situations when for some parts of uncertainty, we know the probability distributions, while for other parts of uncertainty, we only know the bounds (but we do not know the corresponding probabilities). Similarly to the usual Kalman filter, our algorithm is iterative: on each iteration, we first predict the state at the next moment of time, and then we use measurement results to correct the corresponding estimates. On each correction step, we solve a convex optimization problem to find the optimal estimate for the system's state (and the optimal ellipsoid for describing the systems's uncertainty). Testing our algorithm on several highly nonlinear problems has shown that the new algorithm performs the extended Kalman filter technique better-the state estimation technique usually applied to such nonlinear problems.

Organisationseinheit(en)
Geodätisches Institut
Externe Organisation(en)
Hochschule Anhalt
University of Texas at El Paso
Typ
Artikel
Journal
Mathematics
Band
7
Publikationsdatum
03.12.2019
Publikationsstatus
Veröffentlicht
Peer-reviewed
Ja
ASJC Scopus Sachgebiete
Mathematik (insg.)
Elektronische Version(en)
https://doi.org/10.3390/MATH7121168 (Zugang: Offen)
 

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