Clustering-based noise elimination scheme for data pre-processing for deep learning classifier in fingerprint indoor positioning system

Shu Zhi Liu, Rashmi Sharan Sinha, Seung Hoon Hwang

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Wi-Fi-based indoor positioning systems have a simple layout and a low cost, and they have gradually become popular in both academia and industry. However, due to the poor stability of Wi-Fi signals, it is difficult to accurately decide the position based on a received signal strength indicator (RSSI) by using a traditional dataset and a deep learning classifier. To overcome this difficulty, we present a clustering-based noise elimination scheme (CNES) for RSSI-based datasets. The scheme facilitates the region-based clustering of RSSIs through density-based spatial clustering of applications with noise. In this scheme, the RSSI-based dataset is preprocessed and noise samples are removed by CNES. This experiment was carried out in a dynamic environment, and we evaluated the lab simulation results of CNES using deep learning classifiers. The results showed that applying CNES to the test database to eliminate noise will increase the success probability of fingerprint location. The lab simulation results show that after using CNES, the average positioning accuracy of margin-zero (zero-meter error), margin-one (two-meter error), and margin-two (four-meter error) in the database increased by 17.78%, 7.24%, and 4.75%, respectively. We evaluated the simulation results with a real time testing experiment, where the result showed that CNES improved the average positioning accuracy to 22.43%, 9.15%, and 5.21% for margin-zero, margin-one, and margin-two error, respectively.

Original languageEnglish
Article number4349
JournalSensors
Volume21
Issue number13
DOIs
StatePublished - 1 Jul 2021

Keywords

  • Clustering
  • CNN
  • Fingerprint-based indoor positioning
  • RSSI

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