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Combining range and direction for improved localization

Citation Author(s):
Gilles Baechler, Frederike Dümbgen, Golnoosh Elhami, Miranda Kreković, Robin Scheibler, Adam Scholefield, Martin Vetterli
Submitted by:
Miranda Krekovic
Last updated:
13 April 2018 - 5:37am
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Miranda Krekovic
Paper Code:
SAM-P5
 

Self-localization of nodes in a sensor network is typically achieved using either range or direction measurements; in this paper, we show that a constructive combination of both improves the estimation. We propose two localization algorithms that make use of the differences between the sensors’ coordinates, or edge vectors; these can be calculated from measured distances and angles. Our first method improves the existing edge-multidimensional scaling algorithm (E-MDS) by introducing additional constraints
that enforce geometric consistency between the edge vectors. On the other hand, our second method decomposes the edge vectors onto 1-dimensional spaces and introduces the concept of coordinate difference matrices (CDMs) to independently regularize each projection. This solution is optimal when Gaussian noise is added to the edge vectors. We demonstrate in numerical simulations that both algorithms outperform state-of-the-art solutions.

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