Multimodal Learning and Single Source WiFi Based Indoor Localization
Date of Award
M.S. in Computer Engineering
Department of Electrical and Computer Engineering
Advisor: Feng Ye
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 suchas health-care, manufacturing, and security, etc. Wi-Fi fingerprint-based indoor localizationis becoming more and more popluar thanks to pervasive deployment of Wi-Fi access pointsand low maintenance cost. However, Wi-Fi fingerprint-based method requires a databaseof collected signal information from all interest points before hand, which means that theprocess of data preparation is time-consuming and labor-intensive. Meanwhile, due to thedynamic nature of environment, the persistence of localization system is unstable, thusfrequent 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 increasingamount 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 isproposed. The proposed system contains three components: Firstly, a moving object de-tection approach is applied for video processing to generate location labels. Secondly, asingle-source Wi-Fi based localization model is developed using the collected signal dataas well as the autonomously generated location labels. Lastly, a path tracking scheme isproposed to demostrate efficacy of the proposed localization model. Computer based simu-lation results show that the proposed system provides effective solutions to current indoorlocalization problems.
Computer Engineering, Electrical Engineering, deep learning, indoor localization, multimodal learning
Copyright 2020, author
Wu, Hongyu, "Multimodal Learning and Single Source WiFi Based Indoor Localization" (2020). Graduate Theses and Dissertations. 6823.