Distributed Maximum Likelihood for Self-Localization in Sensor Networks

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Executive Summary

The authors show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and they develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, their 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 they show that the developed algorithms are able to learn the localization parameters well.

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