An improved geometric algorithm for indoor localization

Junhua Yang, Yong Li, Wei Cheng

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Indoor localization system using receive signal strength indicator from wireless access point has attracted lots of attention recently. Geometric method is one of the most widely used spatial graph algorithms to locate object in an indoor environment, but it does not achieve good results when it is applied to a limited amount of valid data, especially when using the trilateration method. On the other hand, localization based on fingerprint can achieve high accuracy but need to pay heavy manual labor for fingerprint database establishment. In this article, we propose a bilateral greed iteration localization method based on greedy algorithm in order to use all of the effective anchor points. Comparing to trilateration, fingerprint, and maximum-likelihood method, the bilateral greed iteration method improves the localization accuracy and reduces complexity of localization process. The method proposed, coupled with measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms trilateration and maximum-likelihood receive signal strength indicator–based indoor location methods without using any radio map information nor a complicated algorithm. Extensive experiment results in a Wi-Fi coverage office environment indicate that the proposed bilateral greed iteration method reduces the localization error, 63.55%, 9.93%, and 47.85%, compared to trilateration, fingerprint, and maximum-likelihood method, respectively.

Original languageEnglish
JournalInternational Journal of Distributed Sensor Networks
Volume14
Issue number3
DOIs
StatePublished - 1 Mar 2018

Keywords

  • greedy algorithm
  • Indoor localization
  • K-NN
  • trilateration
  • Wi-Fi

Fingerprint

Dive into the research topics of 'An improved geometric algorithm for indoor localization'. Together they form a unique fingerprint.

Cite this