In this paper we propose a new nonparametric approach to identification of linear time invariant systems using subspace methods. The nonparametric paradigm to prediction of station...
Alessandro Chiuso, Gianluigi Pillonetto, Giuseppe ...
Due to constraints in cost, power, and communication, losses often arise in large sensor networks. The sensor can be modeled as an output of a linear stochastic system with random...
Alyson K. Fletcher, Sundeep Rangan, Vivek K. Goyal
— Given a fixed network where each node has some given initial value, and under the constraint that each node receives noisy transmissions from its immediate neighbors, we provi...
Abstract— In this paper, we consider a discrete-time stochastic system, where sensor measurements are sent over a network to the controller. The design objective is a non-classic...
We consider linear models for stochastic dynamics. To any such model can be associated a network (namely a directed graph) describing which degrees of freedom interact under the d...