Mobile Tracking Base On Fractional Integration
Résumé
While the static indoor geo-location of mobile terminals (MT) has been extensively studied in the last decade, the prediction of the trajectory of a MT still is the major problem for designing mobile location systems (TSs). It is important to augment mobile geo-location architectures with a prediction dimension to deal with distortions caused by obstacles, and ultimately produce a more accurate positioning system. Different prediction approaches have been proposed in the literature, the most common is based on prediction filters such as linear predictors, Kalman filters, and particle filters. In this paper, we take the prediction one step further by using digital fractional integration (DFI) to predict the actual trajectory of MTs. We evaluate the performance of our proposed DFI prediction in two indoor trajectory scenarios inspired from typical users mobility patterns in typical indoor conditions. To illustrate the efficiency of the proposed method in particularly noisy environments, we consider two other MT trajectory scenarios, namely spiral and sinusoidal trajectories. Experimental results show a significant performance improvement over most common predictors in the relevant literature, particularly in noisy cases. Extensive study of short-archive principle using 5, 10, and 25 previous estimated positions, showed the benefit of using DFI operator with only the most recent locations of a MT.