In contemporary research, target localization has become a prevalent challenge in
wireless sensor networks (WSN). The localization of targets in WSN is facilitated by
attaching sensors to the target. Such a localization technique is referred to as devicebased
target localization. The device-based localization encounters issues such as difficulty
with elderly monitoring, security, and inaccessibility by non-intended targets.
This opens up the scope for device-free target localization. In device-free localization
(DFL) techniques, no sensor is attached to the target that needs to be localized. Most
importantly, the DFL system can localize targets in WSN without knowledge of the
localization system, which thereby helps with privacy during the localization process.
Therefore, a DFL system can be applied for rescue operations, intrusion detection,
roadside surveillance, and health care systems. Radio tomographic imaging (RTI) is
one such DFL technique that enables the localization of single or multiple targets by
finding the attenuation information of radio waves caused by the targets. The radio
wave attenuation information is provided through a radio map and is known as spatial
loss fields (SLF). Hence, SLF provide the location and shape of the targets in the RTI
system. The estimation of SLF can be done by acquiring the received signal strength
(RSS) information of different sensor nodes in the network. A large change in RSS
is observed when the targets lie in the line-of-sight (LOS) of the link. The change in
RSS information, along with the weight of every pixel in the network, is used for the
estimation of the SLF. Using the RSS value of every sensor node and the weight of each
pixel, the underlying SLF can be found through a linear regression model. Generally,
the number of observed data (RSS) is quite less than the number of predictors (pixels)
of the network, which indicates RTI is an ill-posed problem. Hence, the estimated
SLF obtained from the linear least square algorithm suffers from poor localization and
shape of the targets. Therefore, in the RTI system, appropriate prior information about
the underlying SLF is incorporated through a regularized cost function that enables an
accurate SLF estimation.