IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v16y2020i9p1550147719884893.html
   My bibliography  Save this article

A three-dimensional pattern recognition localization system based on a Bayesian graphical model

Author

Listed:
  • Abdulraqeb Alhammadi
  • Fazirulhisyam Hashim
  • Mohd. Fadlee A Rasid
  • Saddam Alraih

Abstract

Access points in wireless local area networks are deployed in many indoor environments. Device-free wireless localization systems based on available received signal strength indicators have gained considerable attention recently because they can localize the people using commercial off-the-shelf equipment. Majority of localization algorithms consider two-dimensional models that cause low positioning accuracy. Although three-dimensional localization models are available, they possess high computational and localization errors, given their use of numerous reference points. In this work, we propose a three-dimensional indoor localization system based on a Bayesian graphical model. The proposed model has been tested through experiments based on fingerprinting technique which collects received signal strength indicators from each access point in an offline training phase and then estimates the user location in an online localization phase. Results indicate that the proposed model achieves a high localization accuracy of more than 25% using reference points fewer than that of benchmarked algorithms.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:intdis:v:16:y:2020:i:9:p:1550147719884893
    DOI: 10.1177/1550147719884893
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147719884893
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147719884893?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Tuan D Vy & Yoan Shin, 2019. "iBeacon indoor localization using trusted-ranges model," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477188, January.
    3. Haibin Tong & Qingxu Deng & Tianyu Zhang & Yuanguo Bi, 2018. "A low-cost indoor localization system based on received signal strength indicator by modifying trilateration for harsh environments," International Journal of Distributed Sensor Networks, , vol. 14(6), pages 15501477187, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:intdis:v:16:y:2020:i:9:p:1550147719884893. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.