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

Ligang Sun, Hamza Alkhatib, Boris Kargoll, Vladik Kreinovich and Ingo Neumann
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Ligang Sun: Geodätisches Institut Hannover, Leibniz Universität Hannover, 30167 Hannover, Germany
Hamza Alkhatib: Geodätisches Institut Hannover, Leibniz Universität Hannover, 30167 Hannover, Germany
Boris Kargoll: Institut für Geoinformation und Vermessung Dessau, Hochschule Anhalt, 06846 Dessau, Germany
Vladik Kreinovich: Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA
Ingo Neumann: Geodätisches Institut Hannover, Leibniz Universität Hannover, 30167 Hannover, Germany

Mathematics, 2019, vol. 7, issue 12, 1-22

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.

Keywords: Ellipsoidal and Gaussian Kalman filter; state estimation; unknown but bounded uncertainty; nonlinear programming; convex optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
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