A Statistical Indoor Localization Method for Supporting Location-based Access Control

Location awareness is critical for supporting location-based access control (LBAC). The challenge is how to determine locations accurately and efficiently in indoor environments. Existing solutions based on WLAN signal strength either cannot provide high accuracy, or are too complicated in general indoor environments. In this paper, we propose a statistical indoor localization method for supporting location-based access control. In an offline phase, we fit a LOESS [3, 4, 16] local regression model on a training set to build a radio map containing the distribution of signal strength. In an online phase, we estimate locations using Maximum Likelihood Estimation (MLE) [7, 8, 9] based on the measured signal strength and the stored distribution. A Bootstrapping method [11] is further exploited to give a confidence interval of estimation. Compared with others, our method is simpler, more systematic and more accurate. Experimental results show that the average error of our method is less than 2m. Hence, it can better support LBAC applications.