@inproceedings{439a9602851c45968e82041f79f020e4,
title = "Uncertainty minimization in multi-sensor localization systems using model selection theory",
abstract = "Belief propagation methods are the state-of-the-art with multi-sensor state localization problems. However, when localization applications have to deal with multi-modality sensors whose functionality depends on the environment of operation, we understand the need for an inference framework to identify confident and reliable sensors. Such a framework helps eliminate failed/non-functional sensors from the fusion process minimizing uncertainty while propagating belief. We derive a framework inspired from model selection theory and demonstrate results on real world multi-sensor robot state localization and multi-camera target tracking applications.",
author = "Sukumar, \{Sreenivas R.\} and Hamparsum Bozdogan and Page, \{David L.\} and Koschan, \{Andreas F.\} and Abidi, \{Mongi A.\}",
year = "2008",
doi = "10.1109/icpr.2008.4761125",
language = "English",
isbn = "9781424421756",
series = "Proceedings - International Conference on Pattern Recognition",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2008 19th International Conference on Pattern Recognition, ICPR 2008",
}