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Sensor Network Localization by Eigenvector Synchronization Over the Euclidean Group

Author(s): Cucuringu, Mihai; Lipman, Yaron; Singer, Amit

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Abstract: We present a new approach to localization of sensors from noisy measurements of a subset of their Euclidean distances. Our algorithm starts by finding, embedding, and aligning uniquely realizable subsets of neighboring sensors called patches. In the noise-free case, each patch agrees with its global positioning up to an unknown rigid motion of translation, rotation, and possibly reflection. The reflections and rotations are estimated using the recently developed eigenvector synchronization algorithm, while the translations are estimated by solving an overdetermined linear system. The algorithm is scalable as the number of nodes increases and can be implemented in a distributed fashion. Extensive numerical experiments show that it compares favorably to other existing algorithms in terms of robustness to noise, sparse connectivity, and running time. While our approach is applicable to higher dimensions, in the current article, we focus on the two-dimensional case.
Publication Date: Jul-2012
Electronic Publication Date: 1-Jul-2012
Citation: Cucuringu, Mihai, Lipman, Yaron, Singer, Amit. (2012). Sensor Network Localization by Eigenvector Synchronization Over the Euclidean Group. ACM TRANSACTIONS ON SENSOR NETWORKS, 8 (10.1145/2240092.2240093
DOI: doi:10.1145/2240092.2240093
ISSN: 1550-4859
EISSN: 1550-4867
Type of Material: Journal Article
Version: Author's manuscript

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