114 research outputs found
People-Sensing Spatial Characteristics of RF Sensor Networks
An "RF sensor" network can monitor RSS values on links in the network and
perform device-free localization, i.e., locating a person or object moving in
the area in which the network is deployed. This paper provides a statistical
model for the RSS variance as a function of the person's position w.r.t. the
transmitter (TX) and receiver (RX). We show that the ensemble mean of the RSS
variance has an approximately linear relationship with the expected total
affected power (ETAP). We then use analysis to derive approximate expressions
for the ETAP as a function of the person's position, for both scattering and
reflection. Counterintuitively, we show that reflection, not scattering, causes
the RSS variance contours to be shaped like Cassini ovals. Experimental tests
reported here and in past literature are shown to validate the analysis
Joint Ultra-wideband and Signal Strength-based Through-building Tracking for Tactical Operations
Accurate device free localization (DFL) based on received signal strength
(RSS) measurements requires placement of radio transceivers on all sides of the
target area. Accuracy degrades dramatically if sensors do not surround the
area. However, law enforcement officers sometimes face situations where it is
not possible or practical to place sensors on all sides of the target room or
building. For example, for an armed subject barricaded in a motel room, police
may be able to place sensors in adjacent rooms, but not in front of the room,
where the subject would see them. In this paper, we show that using two
ultra-wideband (UWB) impulse radios, in addition to multiple RSS sensors,
improves the localization accuracy, particularly on the axis where no sensors
are placed (which we call the x-axis). We introduce three methods for combining
the RSS and UWB data. By using UWB radios together with RSS sensors, it is
still possible to localize a person through walls even when the devices are
placed only on two sides of the target area. Including the data from the UWB
radios can reduce the localization area of uncertainty by more than 60%.Comment: 9 pages, conference submissio
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
RFID Localization in Wireless Sensor Networks
Received signal strength (RSS)‐based localization of people and assets through RFID has significant benefits for logistics, security and safety. However, the accuracy of RFID localization in wireless sensor networks suffers from unrealistic antenna gain pattern assumption, and the human body has a major effect on the gain pattern of the RFID badge that the person is wearing. In this book chapter, the gain pattern due to the effect of the human body is experimentally measured and modeled. A method is presented to estimate the model parameters from multiple RSS measurements. Two joint orientation and position estimators, four‐dimensional (4D) maximum likelihood estimation (MLE) algorithm and alternating gain and position estimation (AGAPE) algorithm, are proposed to estimate the orientation and the position of the badge using RSS measurements from anchor nodes. A Bayesian lower bound on the mean squared error of the joint estimation is derived and compared with the Cramer‐Rao bound with an isotropic gain pattern. Both theoretical and experimental results show that the accuracy of position estimates can be improved with orientation estimates included in the localization system
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