Distributed estimation with information-seeking control in agent networks
Artikel i vetenskaplig tidskrift, 2015

We introduce a distributed cooperative framework and method for Bayesian estimation and control in decentralized agent networks. Our framework combines joint estimation of time-varying global and local states with information-seeking control optimizing the behavior of the agents. It is suited to nonlinear and non-Gaussian problems and, in particular, to location-aware networks. For cooperative estimation, a combination of belief propagation message passing and consensus is used. For cooperative control, the negative posterior joint entropy of all states is maximized via a gradient ascent. The estimation layer provides the control layer with probabilistic information in the form of sample representations of probability distributions. Simulation results demonstrate intelligent behavior of the agents and excellent estimation performance for a simultaneous self-localization and target tracking problem. In a cooperative localization scenario with only one anchor, mobile agents can localize themselves after a short time with an accuracy that is higher than the accuracy of the performed distance measurements.


Agent networks

distributed control

message passing

sensor networks

distributed estimation

sequential estimation

cooperative localization

information-seeking control

distributed target tracking

belief propagation


Florian Meyer

Technische Universitat Wien

Henk Wymeersch

Signaler och system, Kommunikationssystem, informationsteori och antenner, Kommunikationssystem

Markus Fröhle

Signaler och system, Kommunikationssystem, informationsteori och antenner, Kommunikationssystem

Franz Hlawatsch

Technische Universitat Wien

IEEE Journal on Selected Areas in Communications

0733-8716 (ISSN)

Vol. 33 2439-2456 7102683