  Human motion in the vicinity of a wireless link causes variations in the link
received signal strength (RSS). Device-free localization (DFL) systems, such as
variance-based radio tomographic imaging (VRTI), use these RSS variations in a
static wireless network to detect, locate and track people in the area of the
network, even through walls. However, intrinsic motion, such as branches moving
in the wind and rotating or vibrating machinery, also causes RSS variations
which degrade the performance of a DFL system. In this paper, we propose and
evaluate two estimators to reduce the impact of the variations caused by
intrinsic motion. One estimator uses subspace decomposition, and the other
estimator uses a least squares formulation. Experimental results show that both
estimators reduce localization root mean squared error by about 40% compared to
VRTI. In addition, the Kalman filter tracking results from both estimators have
97% of errors less than 1.3 m, more than 60% improvement compared to tracking
results from VRTI.
