Paper Info
Title
Online State Estimation for Time-Varying Systems
Abstract
The article investigates the problem of estimating the state of a time-varying system with a linear measurement model; in particular, the article considers the case where the number of measurements available can be smaller than the number of states. In lieu of a batch linear least-squares approach—well-suited for static networks, where a sufficient number of measurements could be collected to obtain a full-rank design matrix—the article proposes an online algorithm to estimate the possibly time-varying state by processing measurements as and when available. The design of the algorithm hinges on a generalized least-squares cost augmented with a proximal-point-type regularization. With the solution of the regularized least-squares problem available in closed-form, the online algorithm is written as a linear dynamical system where the state is updated based on the previous estimate and based on the new available measurements. Conditions under which the algorithmic steps are in fact a contractive mapping are shown, and bounds on the estimation error are derived for different noise models. Numerical simulations are provided to corroborate the analytical findings.
Year
DOI
Venue
2022
10.1109/TAC.2021.3120679
IEEE Transactions on Automatic Control
Keywords
DocType
Volume
Asynchronous sensors,networked systems,state estimation,time-varying systems
Journal
67
Issue
ISSN
Citations 
10
0018-9286
0
PageRank 
References 
Authors
0.34
11
4
Name
Order
Citations
PageRank
Guido Cavraro16910.25
Emiliano Dall'Anese236038.11
Joshua Comden300.68
Andrey Bernstein4298.99