Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
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.
How to cite this article:
M.C. Su , C.Y. Li , D.Y. Huang , S.C. Lin , G.D. Chen , C.C. Hsieh and P.C. Wang . A Regression-Model-Based Approach to Indoor Location Estimation.
DOI: https://doi.org/10.36478/jeasci.2008.307.311
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2008.307.311