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We introduce a distributed, cooperative framework and method for Bayesianestimation and control in decentralized agent networks. Our framework combinesjoint estimation of time-varying global and local states withinformation-seeking control optimizing the behavior of the agents. It is suitedto nonlinear and non-Gaussian problems and, in particular, to location-awarenetworks. For cooperative estimation, a combination of belief propagationmessage passing and consensus is used. For cooperative control, the negativeposterior joint entropy of all states is maximized via a gradient ascent. Theestimation layer provides the control layer with probabilistic information inthe form of sample representations of probability distributions. Simulationresults demonstrate intelligent behavior of the agents and excellent estimationperformance for a simultaneous self-localization and target tracking problem.In a cooperative localization scenario with only one anchor, mobile agents canlocalize themselves after a short time with an accuracy that is higher than theaccuracy of the performed distance measurements.

Distributed Estimation with Information-Seeking Control in Agent Network