Hybrid positioning data fusion in heterogeneous networks with critical hearability

In this paper, we propose and investigate a hybrid positioning data fusion technique for heterogeneous networks in critical transmission scenarios. The focus is on two scenarios: the small indoor scenario combining Wi-Fi and cellular systems and the small-to-mid-scale scenario composed of one located Mobile Terminal (MT) and one anchor node (AN). More specifically, we investigate the effect of the availability of three metrics i.e. the time of arrival (ToA), the angle of arrival (AoA), and the received signal strength-based fingerprint (RSS) on the positioning accuracy when the number of ANs is less than three. To combine these measurements, we use a 2-level unscented Kalman Filter (UKF) in conjunction with some advanced clustering techniques based on genetic algorithms. Simulation results show that the proposed hybrid data fusion technique outperforms the techniques presented in the literature independently of the transmission conditions.