Kantas, N and Singh, SS and Doucet, A (2012) Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks. IEEE Transactions on Signal Processing, 60. pp. 5038-5047. ISSN 1053-587XFull text not available from this repository.
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 allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. 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 demonstrate that the developed algorithms are able to learn the localization parameters. © 2012 IEEE.
|Uncontrolled Keywords:||Collaborative tracking Maximum likelihood Sensor localization Sensor networks Target tracking|
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Unnamed user with email firstname.lastname@example.org|
|Date Deposited:||16 Jul 2015 13:37|
|Last Modified:||26 Jul 2015 00:58|