1,235 research outputs found
Dial It In: Rotating RF Sensors to Enhance Radio Tomography
A radio tomographic imaging (RTI) system uses the received signal strength
(RSS) measured by RF sensors in a static wireless network to localize people in
the deployment area, without having them to carry or wear an electronic device.
This paper addresses the fact that small-scale changes in the position and
orientation of the antenna of each RF sensor can dramatically affect imaging
and localization performance of an RTI system. However, the best placement for
a sensor is unknown at the time of deployment. Improving performance in a
deployed RTI system requires the deployer to iteratively "guess-and-retest",
i.e., pick a sensor to move and then re-run a calibration experiment to
determine if the localization performance had improved or degraded. We present
an RTI system of servo-nodes, RF sensors equipped with servo motors which
autonomously "dial it in", i.e., change position and orientation to optimize
the RSS on links of the network. By doing so, the localization accuracy of the
RTI system is quickly improved, without requiring any calibration experiment
from the deployer. Experiments conducted in three indoor environments
demonstrate that the servo-nodes system reduces localization error on average
by 32% compared to a standard RTI system composed of static RF sensors.Comment: 9 page
Device-free Localization using Received Signal Strength Measurements in Radio Frequency Network
Device-free localization (DFL) based on the received signal strength (RSS)
measurements of radio frequency (RF)links is the method using RSS variation due
to the presence of the target to localize the target without attaching any
device. The majority of DFL methods utilize the fact the link will experience
great attenuation when obstructed. Thus that localization accuracy depends on
the model which describes the relationship between RSS loss caused by
obstruction and the position of the target. The existing models is too rough to
explain some phenomenon observed in the experiment measurements. In this paper,
we propose a new model based on diffraction theory in which the target is
modeled as a cylinder instead of a point mass. The proposed model can will
greatly fits the experiment measurements and well explain the cases like link
crossing and walking along the link line. Because the measurement model is
nonlinear, particle filtering tracing is used to recursively give the
approximate Bayesian estimation of the position. The posterior Cramer-Rao lower
bound (PCRLB) of proposed tracking method is also derived. The results of field
experiments with 8 radio sensors and a monitored area of 3.5m 3.5m show that
the tracking error of proposed model is improved by at least 36 percent in the
single target case and 25 percent in the two targets case compared to other
models.Comment: This paper has been withdrawn by the author due to some mistake
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