  RSS-based device-free localization (DFL) monitors changes in the received
signal strength (RSS) measured by a network of static wireless nodes to locate
people without requiring them to carry or wear any electronic device. Current
models assume that the spatial impact area, i.e., the area in which a person
affects a link's RSS, has constant size. This paper shows that the spatial
impact area varies considerably for each link. Data from extensive experiments
are used to derive a multi-scale spatial weight model that is a function of the
fade level, i.e., the difference between the predicted and measured RSS, and of
the direction of RSS change. In addition, a measurement model is proposed which
gives a probability of a person locating inside the derived spatial model for
each given RSS measurement. A real-time radio tomographic imaging system is
described which uses channel diversity and the presented models. Experiments in
an open indoor environment, in a typical one-bedroom apartment and in a
through-wall scenario are conducted to determine the accuracy of the system. We
demonstrate that the new system is capable of localizing and tracking a person
with high accuracy (<0.30 m) in all the environments, without the need to
change the model parameters.
