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Multimodal Learning and Single Source WiFi Based Indoor Localization

Wu, Hongyu
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1588098876967656

Abstract Details

2020, Master of Science in Computer Engineering, University of Dayton, Electrical and Computer Engineering.
With the rapid development of high speed Internet and Internet of Things (IoT) ap- plications, the demand of indoor localization technology is increasing over years. Well- developed indoor localization technologies can bring significant changes to industries such as health-care, manufacturing, and security, etc. Wi-Fi fingerprint-based indoor localization is becoming more and more popluar thanks to pervasive deployment of Wi-Fi access points and low maintenance cost. However, Wi-Fi fingerprint-based method requires a database of collected signal information from all interest points before hand, which means that the process of data preparation is time-consuming and labor-intensive. Meanwhile, due to the dynamic nature of environment, the persistence of localization system is unstable, thus frequent data re-acquistion and model re-modeling are needed. In addition, current Wi- Fi fingerprint-based methods require multiple WiFi sources, which leads to the increasing amount of cost when constructing the localization system. Therefore, to tackle these is- sues, a multimodal learning and single source Wi-Fi based indoor localization system is proposed. The proposed system contains three components: Firstly, a moving object de- tection approach is applied for video processing to generate location labels. Secondly, a single-source Wi-Fi based localization model is developed using the collected signal data as well as the autonomously generated location labels. Lastly, a path tracking scheme is proposed to demostrate efficacy of the proposed localization model. Computer based simu- lation results show that the proposed system provides effective solutions to current indoor localization problems.
Feng Ye (Advisor)
Bradley Rat;off (Committee Member)
Vijayan Asari (Committee Member)
47 p.
Computer Engineering; Electrical Engineering
deep learning, indoor localization, multimodal learning

Recommended Citations

Citations

  • Wu, H. (2020). Multimodal Learning and Single Source WiFi Based Indoor Localization [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1588098876967656

    APA Style (7th edition)

  • Wu, Hongyu. Multimodal Learning and Single Source WiFi Based Indoor Localization. 2020. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1588098876967656.

    MLA Style (8th edition)

  • Wu, Hongyu. "Multimodal Learning and Single Source WiFi Based Indoor Localization." Master's thesis, University of Dayton, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1588098876967656

    Chicago Manual of Style (17th edition)

dayton1588098876967656
505
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This open access ETD is published by University of Dayton and OhioLINK.