Title :
Spring Model Based Collaborative Indoor Position Estimation With Neighbor Mobile Devices
Author :
Taniuchi, Daisuke ; Xiaopeng Liu ; Nakai, Daisuke ; Maekawa, Takuya
Author_Institution :
Grad. Sch. of Inf. Sci. & Technol., Osaka Univ., Suita, Japan
Abstract :
Nowadays, as the widespread of smart-phones that equipped with Wi-Fi modules, many researchers have studied Wi-Fi based indoor positioning techniques. The existing method makes use of the Wi-Fi received signal strength (RSS) information that collected from several places indoors in advance to estimate the position of a mobile device by referring to a fingerprinting algorithm. Based on the existing method, this paper addresses a high-precision collaborative indoor positioning method. We first estimate the position coordinates of neighbor mobile devices and the distances between the devices by using the Wi-Fi and Bluetooth sensors on them. Then, by making use of the position and distance information, we utilize the spring model to correct the positioning errors. In addition, we performed the evaluation experiment in a real indoor environment, and confirmed the feasibility of our proposed method.
Keywords :
Bluetooth; RSSI; indoor communication; mobile radio; wireless LAN; Bluetooth sensors; RSS information; Wi-Fi; collaborative indoor position estimation; distance information; fingerprinting algorithm; indoor positioning technique; neighbor mobile devices; received signal strength; spring model; Accuracy; Bluetooth; Collaboration; Estimation; IEEE 802.11 Standards; Mobile handsets; Springs; Indoor positioning; collaborative positioning; fingerprinting; neighbor mobile devices; received signal strength (RSS); spring model;
Journal_Title :
Selected Topics in Signal Processing, IEEE Journal of
DOI :
10.1109/JSTSP.2014.2382478