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The paper presents the gossip interactive Kalman filter (GIKF) for distributed Kalman filtering for networked systems and sensor networks, where intersensor communication and observations occur at the same time-scale. The communication among sensors is random; each sensor occasionally exchanges its filtering state information with a neighbor depending on the availability of the appropriate network...
This paper introduces higher dimensional consensus, a framework to capture a number of different, but, related distributed, iterative, linear algorithms of interest in sensor networks. We show that, by suitably choosing the iteration matrix of the higher dimensional consensus, we can capture, besides the standard average-consensus, a broad range of applications, including sensor localization, leader-follower,...
The paper considers the problem of distributed linear vector parameter estimation in sensor networks, when sensors can exchange quantized state information and the inter-sensor communication links fail randomly. We show that our algorithm LU leads to almost sure (a.s.) consensus of the local sensor estimates to the true parameter value, under the assumptions that, a minimal global observability criterion...
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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