  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.
