Advances in wireless localization techniques aiming to exploit context-dependent data has been leading to a growing interest in services able of localizing or tracking targets inside buildings with high accuracy and precision. Hence, the demand for indoor localization services has become a key prerequisite in some markets, such as in the aviation sector. In this context, we propose a system to passively localize and track passenger movements inside the cabin of an aircraft in a privacy preserving way using existing communication networks such as Wi-Fi or 5G. The estimated passenger positions can be used for various automation tasks such as measurement of passenger behavior during boarding. The paper describes a novel wireless localization system, based on Artificial Neural Networks, which passively senses the location of passengers. The position estimation is based on the observation of wireless communication signals that are already present in the environment. In this context, "passive" means that no additional devices are needed for the passengers. Experimental results show that the proposed system is able to achieve an average accuracy of 12 cm in a challenging environment like an aircraft cabin. This accuracy seems sufficient to control passenger separation.
