Visible light-based robust positioning under detector orientation uncertainty: A gabor convolutional network-based approach extracting stable texture features

Bingpeng Zhou, Risto Wichman

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

Abstrakti

In this paper, we are interested in visible light-based positioning (VLP) of detectors with unknown orientations. Conventional VLP methods depend on a well-defined signal propagation model (SPM) with perfectly known or estimated parameters. Thus, uncertainty of detector orientation degrades their VLP performance. To address this challenge, we propose a machine learning (ML)-based VLP solution, which comprises a Gabor convolutional neural network (GCNN) and a fully-connected neural network (FCNN). We observe spatial texture structures in received visible light signals, which depend on the detector location, and hence can be exploited to enhance VLP performance. GCNN extracts rotation-invariant features of visible light samples under uncertain detector orientations' using diverse Gabor kernels. FCNN captures informative clustering structures of obtained texture features. Unlike SPM-based VLP methods, our ML-based VLP is a data-driven solution, which depends on clustering structure of received signals and their features, and hence no longer needs a perfect SPM. It is shown that the proposed ML-based VLP method outperforms the conventional VLP baselines.

AlkuperäiskieliEnglanti
OtsikkoProceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020
KustantajaIEEE
Sivumäärä6
ISBN (elektroninen)9781728166629
DOI - pysyväislinkit
TilaJulkaistu - syyskuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Workshop on Machine Learning for Signal Processing - Espoo, Suomi
Kesto: 21 syyskuuta 202024 syyskuuta 2020
Konferenssinumero: 30
https://ieeemlsp.cc

Julkaisusarja

NimiIEEE International Workshop on Machine Learning for Signal Processing
ISSN (painettu)2161-0363
ISSN (elektroninen)2161-0371

Workshop

WorkshopIEEE International Workshop on Machine Learning for Signal Processing
LyhennettäMLSP
Maa/AlueSuomi
KaupunkiEspoo
Ajanjakso21/09/202024/09/2020
www-osoite

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