Mobile indoor location based on fractional differentiation

While the static indoor location of a mobile terminal (MT) has been extensively studied on last decade, the prediction of the trajectory of a MT still is the major problem for building mobile location (tracking) systems (TSs). This problem is solved for outdoor TSs using global positioning system (GPS), however, it remains an essential obstacle to construct reliable indoor TSs. Different approaches were proposed in the literature, the most used is that based on prediction filters, such as linear filters (LF), Kalman filters (KF) and particle filters (PF). In this paper, we propose to enhance the performance of the predictors using digital fractional differentiation (DFD) to predict a MT trajectory. To illustrate the obtained results, three indoor trajectory scenarios inspired from real daily promenades are simulated (museum visit, hospital doctor walking and shopping in the market). Experimental results show a significant improvement of the performance of the classical predictors, particularly in noisy cases.