  The indoor radio propagation channel is typically modeled as a two-state
time-variant process where one of the states represents the channel when the
environment is static, whereas the other state characterizes the medium when it
is altered by people. In this paper, the aforementioned process is augmented
with an additional state. It is shown that the changes in received signal
strength are dictated by: i) electronic noise, when a person is not present in
the monitored area; ii) reflection, when a person is moving in the close
vicinity of the line-of-sight; iii) shadowing, when a person is obstructing the
line-of-sight component of the transmitter-receiver pair. Statistical and
spatial models for the three states are derived and the models are empirically
validated. Based on the models, a simplistic device-free localization
application is designed which aims to: first, estimate the temporal state of
the channel using a hidden Markov model; second, track a person using a
particle filter. The results suggest that the tracking accuracy is enhanced by
at least 65% while the link's sensitivity region is increased by 100% or more
with respect to empirical models presented in earlier works.
