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• W2280474529 abstract "In the last few decades we assisted to an extraordinary expansion of the Internet and of wireless technologies. These interconnection technologies allow a continuously increasing number of devices to exchange information. This fact, together with the parallel increase in the availability of inexpensive nodes carrying a wide range of sensing capabilities, attracts the interest in developinglarge-scale sensing platforms, which could be used to measure a variety of physical phenomena. However, these huge networks of simple devices are subject to tight energy and bandwidth constraints, making efficient distributed estimation and data fusion algorithms a strong need, to avoid unmanageable computational and communicational burden on network bottleneck nodes.%In this thesis we address some issues in this research avenueIn this thesis we address some issues in this field, presenting and analyzing distributed algorithms to solve specific distributed estimation problems and carrying out the analysis of some other recently-proposed algorithms.To perform data fusion in a distributed fashion, we relay on consensus algorithms, namely algorithms that achieve agreement on a common value in the network. Using consensus as a basic brick to build estimation algorithms we can take advantage of the solid understanding on this problem that many recent contributions deepened and sharpened, and we can leverage for our analysis on powerful and effective tools. In the thesis we propose a distributed algorithm for offset removal and an algorithm for least-square identification of the wireless-channel parameters, motivated by the application of localization and tracking of a moving object. We present moreover a novel linear algebra inequality, useful in the analysis of randomized algorithms. This result comes into play when we carry out an analysis of a recently-proposed distributed Kalman filtering algorithm. Finally, we look at the intriguing set up of a network cooperation to estimate different but correlated quantities, proposing and analyzing a distributed algorithm that performs inference over a simple Gauss-Markov random field." @default.
• W2280474529 created "2016-06-24" @default.
• W2280474529 creator A5013219153 @default.
• W2280474529 date "2010-01-01" @default.
• W2280474529 modified "2023-09-23" @default.
• W2280474529 title "Analysis and Development of Consensus-based Estimation Schemes" @default.
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