  Radio frequency (RF) sensing networks are a class of wireless sensor networks
(WSNs) which use RF signals to accomplish tasks such as passive device-free
localization and tracking. The algorithms used for these tasks usually require
access to measurements of baseline received signal strength (RSS) on each link.
However, it is often impossible to collect this calibration data (measurements
collected during an offline calibration period when the region of interest is
empty of targets). We propose adapting background subtraction methods from the
field of computer vision to estimate baseline RSS values from measurements
taken while the system is online and obstructions may be present. This is done
by forming an analogy between the intensity of a background pixel in an image
and the baseline RSS value of a WSN link and then translating the concepts of
temporal similarity, spatial similarity and spatial ergodicity which underlie
specific background subtraction algorithms to WSNs. Using experimental data, we
show that these techniques are capable of estimating baseline RSS values with
enough accuracy that RF tomographic tracking can be carried out in a variety of
different environments without the need for a calibration period.
