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ResEntSG: restoration entropy estimation for dynamical systems via Riemannian metric optimization
Version 2 2023-06-12, 09:56
Version 1 2023-06-10, 00:22
journal contribution
posted on 2023-06-12, 09:56 authored by Christoph Kawan, Sigurdur Freyr Hafstein, Peter GieslPeter GieslIn 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.
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- Published
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SoftwareXISSN
2352-7110Publisher
ElsevierExternal DOI
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15Page range
1-5Article number
a100743Department affiliated with
- Mathematics Publications
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- Yes
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2021-07-16First Open Access (FOA) Date
2021-07-16First Compliant Deposit (FCD) Date
2021-07-15Usage metrics
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