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International audienceIn this paper, we propose and investigate a hybrid positioning data fusion technique for heterogeneous networks incritical transmission scenarios. The focus is on two scenarios: the small indoor scenario combining Wi-Fi andcellular systems and the small-to-mid-scale scenario composed of one located Mobile Terminal (MT) and oneanchor node (AN). More specifically, we investigate the effect of the availability of three metrics i.e. the time ofarrival (ToA), the angle of arrival (AoA), and the received signal strength-based fingerprint (RSS) on the positioning accuracywhen 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 thatthe proposed hybrid data fusion technique outperforms the techniques presented in the literature independently of thetransmission conditions

Hybrid positioning data fusion in heterogeneous networks with critical hearability