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Expectation–maximisation based distributed estimation in sensor networks

Author
López, R.; Pagès-Zamora, A.
Type of activity
Book chapter
Book
Data fusion in wireless sensor networks: a statistical signal processing perspective
First page
201
Last page
230
Publisher
Institution of Engineering and Technology
Date of publication
2019-03-01
ISBN
9781785615849 Open in new window
DOI
10.1049/PBCE117E
Repository
http://hdl.handle.net/2117/166887 Open in new window
URL
https://digital-library.theiet.org/content/books/ce/pbce117e Open in new window
Abstract
Estimating the unknown parameters of a statistical model based on the observations collected by a sensor network is an important problem with application in multiple fields. In this setting, distributed processing, by which computations are carried out within the network in order to avoid raw data transmission to a fusion centre, is a desirable feature resulting in improved robustness and energy savings. In the presence of incomplete data, the expectation-maximisation (EM) algorithm is a popular...
Citation
López, R.; Pagès-Zamora, A. Expectation–maximisation based distributed estimation in sensor networks. A: "Data fusion in wireless sensor networks: a statistical signal processing perspective". Institution of Engineering and Technology, 2019, p. 201-230.
Keywords
Expectation-maximisation algorithm, Iterative methods, Learning (artificial intelligence), Maximum likelihood estimation, Motion estimation, Telecommunication network routing, Wireless sensor networks
Group of research
SPCOM - Signal Processing and Communications Group

Participants