A Comparison between Vector Algorithm and CRSS Algorithms for Indoor Localization using Received Signal Strength

Authors

  • Huthifa A. Obeidat School of Engineering and Informatics University of Bradford, Bradford, UK, BD7 1DP
  • Yousif A. S. Dama 1 School of Engineering and Informatics University of Bradford, Bradford, UK, BD7 1DP, 2 Department of Telecommunication Engineering, An Najah National University, Nablus, Palestine
  • Raed A. Abd-Alhameed School of Engineering and Informatics University of Bradford, Bradford, UK, BD7 1DP
  • Yim F. Hu School of Engineering and Informatics University of Bradford, Bradford, UK, BD7 1DP
  • Rami Qahwaji School of Engineering and Informatics University of Bradford, Bradford, UK, BD7 1DP
  • James M. Noras School of Engineering and Informatics University of Bradford, Bradford, UK, BD7 1DP
  • Steven M. R. Jones School of Engineering and Informatics University of Bradford, Bradford, UK, BD7 1DP

Keywords:

CRSS, indoor localization, ray tracing, RSS

Abstract

A comparison is presented between two indoor localization algorithms using received signal strength, namely the vector algorithm and the Comparative Received Signal Strength (CRSS) algorithm. Signal values were obtained using ray tracing software and processed with MATLAB to ascertain the effects on localization accuracy of radio map resolution, number of access points and operating frequency. The vector algorithm outperforms the CRSS algorithm, which suffers from ambiguity, although that can be reduced by using more access points and a higher operating frequency. Ambiguity is worsened by the addition of more reference points. The vector algorithm performance is enhanced by adding more access points and reference points while it degrades with increasing frequency provided that the statistical mean of error increased to about 60 cm for most studied cases.

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Published

2021-08-18

How to Cite

[1]
Huthifa A. Obeidat, “A Comparison between Vector Algorithm and CRSS Algorithms for Indoor Localization using Received Signal Strength”, ACES Journal, vol. 31, no. 08, pp. 868–876, Aug. 2021.

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General Submission