Noisy Euclidean distance matrix completion with a single missing node

Stefan Sremac, Fei Wang (), Henry Wolkowicz and Lucas Pettersson
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Stefan Sremac: University of Waterloo
Fei Wang: Royal Institute of Technology
Henry Wolkowicz: University of Waterloo
Lucas Pettersson: Royal Institute of Technology

Journal of Global Optimization, 2019, vol. 75, issue 4, No 4, 973-1002

Abstract: Abstract 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.

Keywords: Single source localization; Noise; Euclidean distance matrix completion; Semidefinite programming; Wireless communication; Facial reduction; Generalized trust region subproblem; 90C22; 15A83; 90C20; 62P30 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10898-019-00825-7

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