Institute of Information Theory and Automation

Distributed dynamic estimation in diffusion networks

Project leader: Ing. Kamil Dedecius, Ph.D.
Department: AS
Supported by (ID): GP14-06678P
Grantor: Czech Science Foundation
Type of project: theoretical
Duration: 2014 - 2016
More info: here
Publications at UTIA: list


The project aims to develop a dynamic distributed estimation framework, intended for fully distributed low-cost parameter estimation of stationary signals and reduced-complexity tracking of nonstationary processes. Being designed for diffusion networks, where each network node can use information provided by neighbor nodes, it will not rely on the existence of a dedicated fusion center, nor a Hamiltonian cycle. The framework will be formulated abstractly in the Bayesian paradigm, allowing, in contrast to current single problem-oriented methods, its direct application to a large class of different problems, comprising dynamic distributed (auto) regression, classification, reliability estimation etc. The developed methods will be efficient in terms of computational and communication resources. Their robustness to network elements degradation and failures is an inherent part of the solution.
Responsible for information: AS
Last modification: 02.04.2014
Institute of Information Theory and Automation