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An improved geometric algorithm for indoor localization

Author

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  • Junhua Yang
  • Yong Li
  • Wei Cheng

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.

Suggested Citation

  • Junhua Yang & Yong Li & Wei Cheng, 2018. "An improved geometric algorithm for indoor localization," International Journal of Distributed Sensor Networks, , vol. 14(3), pages 15501477187, March.
    • Handle: RePEc:sae:intdis:v:14:y:2018:i:3:p:1550147718767376
    DOI: 10.1177/1550147718767376
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    Cited by:

    1. Stenfors, Alexis & Susai, Masayuki, 2021. "Spoofing and pinging in foreign exchange markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 70(C).
    2. Abdulraqeb Alhammadi & Fazirulhisyam Hashim & Mohd. Fadlee A Rasid & Saddam Alraih, 2020. "A three-dimensional pattern recognition localization system based on a Bayesian graphical model," International Journal of Distributed Sensor Networks, , vol. 16(9), pages 15501477198, September.

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