Mobility tracking by fingerprint-based KNN/PF approach in cellular networks

In this paper, we present a fingerprint-based particle filtering (PF) approach for the mobility tracking in cellular networks. With the popularity of location-based services (LBSs) in a recent decade, the mobility tracking has now become one of the indispensable techniques to fulfill the pervasive and location-aware computing. However, the tracking results from the conventional K nearest neighbors (KNN) and Kalman filtering (KF) by the cellular data appear to be coarse due to the reflection, refraction and diffraction of received signal code power (RSCP). Therefore, we propose the KNN/PF as an effective way to improve the stability and accuracy of mobility tracking in cellular networks. Furthermore, the experiments conducted in Pudong New District, Shanghai, China, show that the KNN/PF approach can achieve better cumulative density function (CDF) of tracking errors compared with the KNN and the mixed KNN and KF (KNN/KF) approaches.