Kantas, N and Singh, SS and Doucet, A (2009) Distributed maximum likelihood for self-localization in sensor networks. Technical Report. Cambridge University Engineering Department, Cambridge, UK.Full text not available from this repository.
We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.
|Item Type:||Monograph (Technical Report)|
|Additional Information:||Contact email address = email@example.com Report no. CUED/F-INFENG/TR.625, Series ISSN: 09519211.|
|Uncontrolled Keywords:||Distributed inference, sensor localization, recursive maximum likelihood, expectation-maximization, sensor networks|
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Cron Job|
|Date Deposited:||28 Oct 2011 17:06|
|Last Modified:||22 Oct 2012 01:12|
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