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Noisy Euclidean Distance Matrix Completion with a Single Missing Node
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.ORCID iD: 0000-0001-8978-5649
2019 (English)In: Journal of Global Optimization, ISSN 0925-5001, E-ISSN 1573-2916Article in journal (Refereed) Published
Abstract [en]

We present several solution techniques for the noisy single source localization problem, i.e.,~the Euclidean distance matrix completion problem with a single missing node to locate under noisy data. For the case that the sensor locations are fixed, we show that this problem is implicitly convex, and we provide a purification algorithm along with the SDP relaxation to solve it efficiently and accurately. For the case that the sensor locations are relaxed, we study a model based on facial reduction. We present several approaches to solve this problem efficiently, and we compare their performance with existing techniques in the literature. Our tools are semidefinite programming, Euclidean distance matrices, facial reduction, and the generalized trust region subproblem. We include extensive numerical tests.

Place, publisher, year, edition, pages
2019.
Keywords [en]
single source localization, noise, Euclidean distance matrix completion, semidefinite programming, wireless communication, facial reduction, generalized trust region subproblem
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-256326OAI: oai:DiVA.org:kth-256326DiVA, id: diva2:1344747
Note

QC 20190823

Available from: 2019-08-21 Created: 2019-08-21 Last updated: 2019-08-23Bibliographically approved

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