Home > Archive > 2019 > Volume 9 Number 2 (Apr. 2019) >
IJMLC 2019 Vol.9(2): 236-241 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.2.792

Graphical User Interface (GUI) for Local Positioning System Based on Labview

Hameedah Sahib Hasan, Mohamed Hussein, Shaharil Mad Saad, and Mohd Azuwan Mat Dzahir

Abstract—Local position system (LPS) becomes more and more diversified and increasingly important in large numbers of applications and contexts such as healthcare, targeted, monitoring, tracking and security. In LPS, three anchors at least with known position are employed with one tag for localization. In this paper, LPS which contains three anchors and one tag is used to get positioning. Tag position represents user location. It estimates by using UWB technology which sending signal based on triangulation method with assist of Labview. By using Labview coding the position of each anchors can be read successful after connection all anchors and tag with PC and router. The positioning of tag in (x, y) direction, successfully update immediately when tag moving which is shown in graph. Therefore, the Graphical User Interface (GUI) for LPS based on Labview is achieved to determine the user positioning with positioning error less than 17cm. We believe this paper would catalyze further investigation by the researcher which deal with both of LPS field and Labview.

Index Terms—Local positioning system, anchors, tag, UWB, graphical user interface, Labview.

Hameedah Sahib Hasan is with Ministry of Higher Education and Scientific Research, Al-Furat Al-Awsat Technical University, Iraq (e-mail: hameedah211ou@gmail.com).
Mohamed Hussein, Shaharil Mad Saad,, and Mohd Azuwan Mat Dzahir are with Faculty of Mechanical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bauru, 81310, Malaysia (Corresponding author: Shaharil Mad Saad; e-mail: shaharil@utm.my).


Cite: Hameedah Sahib Hasan, Mohamed Hussein, Shaharil Mad Saad, and Mohd Azuwan Mat Dzahir, "Graphical User Interface (GUI) for Local Positioning System Based on Labview," International Journal of Machine Learning and Computing vol. 9, no. 2, pp. 236-241, 2019.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net
  • APC: 500USD

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