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

authored by
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.

Organisation(s)
Geodetic Institute
External Organisation(s)
Anhalt University of Applied Sciences
University of Texas at El Paso
Type
Article
Journal
Mathematics
Volume
7
Publication date
03.12.2019
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
Mathematics(all)
Electronic version(s)
https://doi.org/10.3390/MATH7121168 (Access: Open)
 

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