Develops a framework for state-space estimation when the parameters of the underlying
linear model are subject to uncertainties. Compared with existing robust filters, the proposed …

State estimation plays a fundamental role in control systems that rely on the knowledge of
the underlying system state, especially when it is not readily available. The Kalman filter is …

Traditional multiuser receiver algorithms developed for multiple-input-multiple-output
(MIMO) wireless systems are based on the assumption that the channel state information …

Modeling and control of dynamic systems that experience abrupt changes are challenging
and fundamental tasks in applications of different areas of engineering. Among these …

We study regression problems in which an adversary can exercise some control over the
data generation process. Learner and adversary have conflicting but not necessarily …

In this paper we deal with an optimal filtering problem for uncertain discrete-time systems.
Parametric uncertainties of the underlying model are assumed to be norm bounded. We …

In this paper, a new distributed robust filtering problem is investigated for a class of discrete-
time systems subject to parameter uncertainty over sensor networks under the event-based …

This note develops robust filters for time-varying uncertain discrete-time systems. The
developed filters are based on a data regularization solution and they enforce a minimum …

When discrete-time Markov jump linear systems are prone to the damaging effects of
polytopic uncertainties, it is necessary to address all the vertices of each Markov mode in …

Bayesian regression games are a special class of two-player general-sum Bayesian games
in which the learner is partially informed about the adversary's objective through a Bayesian …