TY  - JOUR
T1  - A Regression-Model-Based Approach to Indoor Location Estimation
AU - , M.C. Su AU - , C.Y. Li AU - , D.Y. Huang AU - , S.C. Lin AU - , G.D. Chen AU - , C.C. Hsieh AU - , P.C. Wang 
JO  - Journal of Engineering and Applied Sciences
VL  - 3
IS  - 4
SP  - 307
EP  - 311
PY  - 2008
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2008.307.311
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2008.307.311
KW  - Regression
KW  -model
KW  -indoor location
KW  -estimation
AB  - Recently, context-aware or location-aware computing has become an interesting research field and has many practical applications in commerce, tourism, public safety, entertainment, military environments, hospital management, etc. Many different approaches have been proposed to tackle the problem of determining the location of a user or a mobile device. In an outdoor environment, the Global Positioning System (GPS) is the most popular solution. However, due to the poor indoor coverage, the GPS cannot provide a satisfactory solution to the problem of indoor location estimation. Many different approaches have been proposed to tackle the indoor location estimation problem. In this study, by use of the Received Signal Strength Indication (RSSI) measurements, a simple approaches to indoor location estimation are introduced to provide a simple but effective solution to the indoor localization problem based on existing wireless LAN infrastructures. The approach is based on regression models. The performance of the proposed approaches is demonstrated by testing 2 data sets acquired from real-world environments.
ER  -