Most range-free techniques for indoor localization depend on the received signal strength (RSS) fingerprints. Their performances are relied to the structure of the considered indoor environments. We consider in this paper RSS-based methods: Multi-Layer Perceptron Neural Network (MLPNN), and K-nearest neighbor (KNN), and compare their performance under the same indoor environment. One of the advantages focused by the choice of these techniques is their robustness against external disturbances that may affect the received RSS signal. Moreover, we propose a new metric to enhance the performance of the KNN method, called d-nearest neighbor. In order to test the different techniques, we build a heterogeneous fingerprint database with different resolutions. The obtained results show the efficiency of the proposed enhancement in the case of a heterogeneous high resolution database.

While the static indoor geo-location of mobile terminals (MTs) has been extensively studied in the last decade, the prediction of the trajectory of an MT is still a major problem when designing mobile location (tracking) systems (TSs). In fact, Global Positioning System (GPS) works quite well in outdoor conditions and relatively unobstructed spaces, but falls short in many urban conditions and other realistic use cases. 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 (LPs), Kalman filters (KFs), and particle filters (PFs). 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 by typical user mobility patterns in typical indoor conditions (museum visit and hospital doctor walk). 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 an MT.

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.