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Distributed Maximum Likelihood for Simultaneous Self-Localization and Tracking in Sensor Networks

Abstract:

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter al...

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Publication status:
Published

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Publisher copy:
10.1109/TSP.2012.2205923

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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Journal:
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume:
60
Issue:
10
Pages:
5038-5047
Publication date:
2012-10-05
DOI:
EISSN:
1941-0476
ISSN:
1053-587X
URN:
uuid:9d103af2-17c5-48e7-a766-cfa16ce5025c
Source identifiers:
354790
Local pid:
pubs:354790

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