Thesis - Open Access
Master of Science (MS)
Electrical Engineering and Computer Science
Indoor localization, Robotic Operating System (ROS), RSSI, Ultra-Wideband (UWB), Unmanned Aerial Vehicles (UAV), WiFi fingerprinting
Explosive growth in the number of mobile devices like smartphones, tablets, and smartwatches has escalated the demand for localization-based services, spurring development of numerous indoor localization techniques. Especially, widespread deployment of wireless LANs prompted ever increasing interests in WiFi-based indoor localization mechanisms. However, a critical shortcoming of such localization schemes is the intensive time and labor requirements for collecting and building the WiFi fingerprinting database, especially when the system needs to cover a large space. In this thesis, we propose to automate the WiFi fingerprint survey process using a group of nano-scale unmanned aerial vehicles (NAVs). The proposed system significantly reduces the efforts for collecting WiFi fingerprints. Furthermore, since these NAVs explore a 3D space, the WiFi fingerprints of a 3D space can be obtained increasing the localization accuracy. The proposed system is implemented on a commercially available miniature open-source quadcopter platform by integrating a contemporary WiFi - fingerprint - based localization system. Experimental results demonstrate that the localization error is about 2m, which exhibits only about 20cm of accuracy degradation compared with the manual WiFi fingerprint survey methods.
Includes bibliographical references
Number of Pages
South Dakota State University
In Copyright - Educational Use Permitted
Chekuri, Appala Narasimha Raju, "Wi-Fi Finger-Printing Based Indoor Localization Using Nano-Scale Unmanned Aerial Vehicles" (2018). Electronic Theses and Dissertations. 2666.