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We introduce a new model of social learning and distributed estimation in which the state to be estimated is governed by a potentially unstable linear model driven by noise. The state is observed by a network of agents, each with its own linear noisy observation models. We assume the state to be globally observable, but no agent is able to estimate the state with its own observations alone. We propose...
This paper presents a distributed sensor localization algorithm with inter-sensor distance information in m-dimensional Euclidean space using only m + 1 anchors (sensors that know their exact locations). Under the assumption that the M sensors (with unknown locations) lie in the convex hull of the m + 1 anchors and the underlying network is connected, we derive a linear algorithm that employs barycentric...
We consider the problem of global average-consensus in a network of sensors. The communication among sensors is restricted by the underlying network, where each sensor can exchange information only with its neighbors. We analyze the influence of the network topology on the convergence speed of the iterative consensus algorithm and focus on the design of network topology with respect to this optimality...
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