Mixture Density Networks for WSN Localization

Julian Karoliny, Bernhard Etzlinger, Andreas Springer

Publikation: KonferenzbeitragPapierBegutachtung

Abstract

A new algorithm for determining soft range information in network localization is proposed. It applies a variant of neural networks called mixture density networks. When used in particle-based Bayesian localization procedures, it has a similar low computational complexity and provides comparable localization accuracy as existing methods. This property enables the proposed algorithm to be implemented on low-power wireless sensor network (WSN) nodes that are equipped with commercial ultra-wideband transceivers. The proposed algorithm is validated in indoor network localization experiments.
OriginalspracheEnglisch
Seiten1-5
Seitenumfang5
DOIs
PublikationsstatusVeröffentlicht - 21 Juli 2020
Veranstaltung2020 IEEE International Conference on Communications Workshops (ICC Workshops) - Dublin, Ireland
Dauer: 7 Juni 202011 Juni 2020

Konferenz

Konferenz2020 IEEE International Conference on Communications Workshops (ICC Workshops)
Zeitraum7/06/2011/06/20

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