Practical Classification Methods for Indoor Positioning

Abstract

Location awareness is of primary importance in a wealth of applications such as transportation, mobile health systems, augmented reality and navigation. For example, in busy transportation areas (such as airports) providing clear, personalised notifications and directions, can reduce delays and improve the passenger journeys. Currently some applications provide easy access to information. These travel related applications can become context aware via the availability of accurate indoor/outdoor positioning. However, there are barriers that still have to overcome. One such barrier is the time required to set up and calibrate indoor positioning systems, another is the challenge of scalability with regard to the processing requirements of indoor positioning algorithms. This paper investigates the relationship between the calibration data and positioning system accuracy and analyses the performance of a k-Nearest Neighbour (k-NN) based positioning algorithm using real GSM data. Furthermore, the paper proposes a positioning scheme based on Gaussian Mixture Models (GMM). Experimental results show that the proposed GMM algorithm (without post-filtering) provides high levels of localization accuracy and successfully copes with the scalability problems that the conventional k- NN approach faces.