Neural network and fingerprinting-based geolocation on time-varying channels

In a harsh indoor environment, fingerprinting geolocation techniques perform better than the traditional ones, based on triangulation, because multipath is used as constructive information. However, this is generally true in static environments as fingerprinting techniques suffer degradations in location accuracy in dynamic environments where the properties of the channel change in time. This is due to the fact that the technique needs a new database collection when a change of the channel's state occurs. In this paper, a novel solution based on a hierarchy of artificial neural networks (ANNs) is proposed to enhance such a geolocation system. It is shown that the enhanced system detects the change in the channel's properties via geolocation reference points, identifies the new channel state and activates a new database that best represents the current radio environment