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

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element