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Indoor Positioning System for Smart Devices

Yang, Yuan
http://rave.ohiolink.edu/etdc/view?acc_num=osu1618789654703694

Abstract Details

2021, Doctor of Philosophy, Ohio State University, Civil Engineering.
With the proliferation of personal smart devices in the last decade, mobile applications of indoor location-based services (ILBS) have been widely used in public buildings, such as hospitals, malls, school campuses and museums for patient monitoring, security management, asset tracking and indoor navigation, etc. As the core component of ILBS, indoor positioning systems (IPS) have gained increased attention. Since global navigation satellite systems (GNSS) are generally denied in indoor environments, as an alternative solution, many Radio Frequency (RF) based approaches of IPS have been proposed. However, these solutions either need to work in a controlled environment with customized RF infrastructure or could only offer low accuracy localization at the meter level. This work focuses on developing an innovative framework of IPS which is able to perform in any indoor environment without extra infrastructure and offers a robust and accurate localization estimation for smart device users. To achieve this goal, this work initially introduces a mean peak method that is subsequently combined with classification-based Wi-Fi fingerprint positioning (WF) techniques to deal with the challenges of positioning in a weak RSS environment. As the next step, the database updating problem for WF, including an innovative Bayes-inference-based sensor fusion framework which integrates WF and visual fingerprint positioning (VF) is addressed. The method provides both a location estimation and a heading direction estimation. In order to further improve the accuracy of the localization, a state-of-the-art visual localization algorithm, InLoc, is introduced in combination with WF for constructing a new IPS. Additionally, with the help of WF, the computational cost of InLoc is reduced compared to the original one. Moreover, to improve the robustness of the system, a particle filter and map updating function are introduced to optimize localization results and address the RSS variance problem. In summary, the proposed methodologies of this work define a framework for constructing an innovative IPS, a new path to build indoor positioning systems which are robust, easy to use and easy to deploy.
Charles Toth, Dr. (Advisor)
Dorota Grejner-Brzezinska, Dr. (Committee Member)
Alper Yilmaz, Dr. (Committee Member)
Rongjun Qin, Dr. (Committee Member)
149 p.
Engineering
indoor positioning; Wi-Fi fingerprinting; visual localization; particle filter; map updating

Recommended Citations

Citations

  • Yang, Y. (2021). Indoor Positioning System for Smart Devices [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1618789654703694

    APA Style (7th edition)

  • Yang, Yuan. Indoor Positioning System for Smart Devices. 2021. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1618789654703694.

    MLA Style (8th edition)

  • Yang, Yuan. "Indoor Positioning System for Smart Devices." Doctoral dissertation, Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1618789654703694

    Chicago Manual of Style (17th edition)

osu1618789654703694
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This open access ETD is published by The Ohio State University and OhioLINK.