Contributions to Proceedings:

O. Hlinka, O. Sluciak, F. Hlawatsch, P. Djuric, M. Rupp:
"Likelihood Consensus: Principles and Application to Distributed Particle Filtering";
in: "Proc. 44th Asilomar Conf. Signals, Systems, Computers", IEEE Conference Proceedings, 2010, (invited).

English abstract:
We propose a distributed method for computing the joint (all-sensors) likelihood function (JLF) in a wireless sensor network. A consensus algorithm is used for a decentralized, iterative calculation of a sufficient statistic that describes an approximation to the JLF. After convergence of the consensus algorithm, the approximate JLF-which epitomizes the measurements of all sensors-is available at each sensor. This "likelihood consensus" method requires only communications between neighboring sensors. We implement the likelihood consensus method in a distributed particle filtering scheme. Each sensor runs a local particle filter that computes a global state estimate. The updating of the particle weights of each local particle filter uses the JLF. The performance of this distributed particle filter is demonstrated on a target tracking problem.

Wireless sensor network, consensus algorithm, Bayesian estimation, distributed particle filter, target tracking

Electronic version of the publication:

Related Projects:
Project Head Franz Hlawatsch:
Signal and Information Processing in Science and Engineering - Statistische Inferenz

Project Head Markus Rupp:
Signal and Information Processing in Science and Engineering - Entwicklungsmethodik

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