Title :
WiFi iLocate: WiFi based indoor localization for smartphone
Author :
Xiang He ; Badiei, Shirin ; Aloi, Daniel ; Jia Li
Author_Institution :
Electr. & Comput. Eng., Oakland Univ., Rochester, MI, USA
Abstract :
In recent years, the increasing popularity of smartphones has promoted the development of location-aware applications. However, highly accurate indoor localization by smartphones remains an open problem. In this paper, we present WiFi iLocate - a system that can help track the location and movement of a smartphone user in indoor environments. The system applies Gaussian process regression to train the collected WiFi received signal strength (RSS) dataset, and particle filter for the estimation of the smartphone user´s location and movement. Simulations were conducted in MATLAB to test the performance and provide more insights of the proposed approach. The experiments carried with an iOS device in typical library environment illustrate that our system is an accurate, real-time, press-to-go system.
Keywords :
Gaussian processes; indoor communication; mobile computing; mobility management (mobile radio); particle filtering (numerical methods); radio tracking; regression analysis; smart phones; smart pixels; wireless LAN; Gaussian process regression; Wi-Fi iLocate; indoor localization; location aware application; location estimation; particle filter; received signal strength; smart phone; Accuracy; Estimation; Fingerprint recognition; Gaussian processes; IEEE 802.11 Standards; Particle filters; Training; Gaussian process regression; WiFi RSS; indoor localization; particle filter; smartphone;
Conference_Titel :
Wireless Telecommunications Symposium (WTS), 2014
Conference_Location :
Washington, DC
DOI :
10.1109/WTS.2014.6835016
