  The performance of multipath-enhanced device-free localization severely
depends on the information about the propagation paths within the network.
While known for the line-of-sight, the propagation paths have yet to be
determined for multipath components. This work provides a novel Bayesian
calibration approach for determining the propagation paths by estimating
reflection points. Therefore, first a statistical fading model is presented,
that describes user-induced changes in the received signal of multipath
components. The model is derived and validated empirically using an extensive
set of wideband and ultra-wideband measurement data. Second, the Bayesian
approach is presented, which, based on the derived empirical fading model,
relates measured changes in the power of a multipath component to the location
of the reflection point. Exploiting the geometric properties of multipath
components caused by single-bounce reflections, the solution space of possible
locations of reflection points is constrained to the delay ellipse. Thus, a
one-dimensional elliptic estimation problem can be formulated, which is solved
using a point mass filter. The applicability of the proposed approach is
demonstrated and evaluated based on measurement data. Independent of the
underlying measurement system, the Bayesian calibration approach is shown to
robustly estimate the locations of the reflection points in different
environments.
