Comparison of Multilateration Methods Using RSSI for Indoor Positioning System

Tong Li, Sijie Ai, Shigeyuki Tateno, Yuriko Hachiya

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

3 Citations (Scopus)

Abstract

Recently, as the indoor positioning technologies have been developed continuously, several indoor positioning methods have been proposed and experimented. A previous research has proposed a statistical access point selection method using a kernel density estimation (KDE) based on received signal strength indicator (RSSI). However, the proposed method is only test through a small area experiment, where the movement of the people beside the experimenters is not so much. In this paper, to analyze the stability of the proposed method under noisy environment, an experiment in a daily healthcare facility work place, where the human movement is more complex, is designed; to analyze the effectiveness of the method, several others multilateration methods are compared with the KDE method. The experiment results show the effectiveness and stability of the proposed method.

Original languageEnglish
Title of host publication2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages371-375
Number of pages5
ISBN (Electronic)9784907764678
DOIs
Publication statusPublished - 2019 Sept
Event58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019 - Hiroshima, Japan
Duration: 2019 Sept 102019 Sept 13

Publication series

Name2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019

Conference

Conference58th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2019
Country/TerritoryJapan
CityHiroshima
Period19/9/1019/9/13

Keywords

  • kernel density estimation
  • multilateration
  • received signal strength indicator

ASJC Scopus subject areas

  • Artificial Intelligence
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Instrumentation

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