ResEntSG: Restoration entropy estimation for dynamical systems via Riemannian metric optimization

Kawan, Christoph; Hafstein, Sigurdur Freyr and Giesl, Peter (2021): ResEntSG: Restoration entropy estimation for dynamical systems via Riemannian metric optimization. In: Softwarex, Vol. 15, 100743

Abstract

In the remote state estimation problem, an observer reconstructs the state of a dynamical system at a remote location, where no direct sensor measurements are available. The estimator only has access to information sent through a digital channel. The notion of restoration entropy provides a way to determine the smallest channel capacity above which an observer can be designed that observes the system without a degradation of the initial estimation error. In general, restoration entropy is hard to compute. We present a class library in C++, that estimates the restoration entropy of a given system by computing an adapted metric for the system. The library is simple to use and implements a version of the subgradient algorithm for geodesically convex functions to compute an optimal metric in a class of conformal metrics. Included in the software are three example systems to demonstrate the use and efficacy of the library. (C) 2021 The Author(s). Published by Elsevier B.V.

• Item Type: Journal article
• Faculties: Mathematics, Computer Science and Statistics > Computer Science
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• ISSN: 2352-7110
• Language: English