Similarity Measures in Development of an Indoor Localization System

Sheng Huang, Syed Shoaib, Andri Ashfahani, Mahardhika Pratama

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

2 Citations (Scopus)

Abstract

One of the issues faced by manufacturing industry is a lack of automatic localization techniques. In this research, a Radio Frequency Identification (RFID) based localization system is proposed for resource tracking. In this study, we incorporated a RFID tag at each item to be tracked, and a RFID reference tag at each location zone. The encoded IDs are read to identify the names of items and location zones. At the same time, radio signals (received signal strength and phase) are measured as RFID fingerprints. Similarity measures are studied to compare fingerprints between RFID item tags and location reference tags to track the location of the items. The kernel-based learning method was implemented as similarity measure. Different cluster labelling methods were compared and it was found that the proximity method is more efficient. The clustering method is used to overcome the issues faced by traditional RFID based localization methods.

Original languageEnglish
Title of host publication2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1420-1425
Number of pages6
ISBN (Electronic)9781538695821
DOIs
Publication statusPublished - 18 Dec 2018
Externally publishedYes
Event15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018 - Singapore, Singapore
Duration: 18 Nov 201821 Nov 2018

Publication series

Name2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018

Conference

Conference15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018
Country/TerritorySingapore
CitySingapore
Period18/11/1821/11/18

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