RSSI-Based Indoor Localization Using Leaky Coaxial Cable with a PNN Approach

Junjie Zhu, Pengcheng Hou, Kenta Nagayama, Yafei Hou, Satoshi Denno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a probabilistic neural network-based (PNN-based) localization method using multiple leaky coaxial cables (LCX) for indoor multipath-rich environment. LCX which can be used as antennas has been applied to wireless communication widely in recent years. Different from conventional localization methods based on time of arrival (ToA) or time difference of arrival (TDoA), we present a probabilistic neural network (PNN) approach by utilizing received signal strength indicator (RSSI) in LCX system. The proposal is aimed at the two-dimensional localization in a trajectory. We evaluate the localization performance of the PNN-based method using RSSI data from LCX. In addition, we also compare the performance of the PNN-based method over the same environment but using conventional monopole antennas. The results show the PNN-based localization method using LCX is promising and is better than that using monopole antennas.

Original languageEnglish
Title of host publication2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages220-221
Number of pages2
ISBN (Electronic)9781665436762
DOIs
Publication statusPublished - 2021
Event10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan
Duration: Oct 12 2021Oct 15 2021

Publication series

Name2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference10th IEEE Global Conference on Consumer Electronics, GCCE 2021
Country/TerritoryJapan
CityKyoto
Period10/12/2110/15/21

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation

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