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

Bingpeng Zhou, Risto Wichman

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020
PublisherIEEE
Number of pages6
ISBN (Electronic)9781728166629
DOIs
Publication statusPublished - Sep 2020
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Espoo, Finland
Duration: 21 Sep 202024 Sep 2020
Conference number: 30
https://ieeemlsp.cc

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Workshop

WorkshopIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP
CountryFinland
CityEspoo
Period21/09/202024/09/2020
Internet address

Keywords

  • Detector orientation uncertainty
  • Gabor CNN
  • Machine learning
  • Visible light-based positioning

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