Paper Info
Title
A Multi-Scale Spatial Model for RSS-based Device-Free Localization
Abstract
RSS-based device-free localization (DFL) monitors changes in the received signal strength (RSS) measured by a network of static wireless nodes to locate people without requiring them to carry or wear any electronic device. Current models assume that the spatial impact area, i.e., the area in which a person affects a link's RSS, has constant size. This paper shows that the spatial impact area varies considerably for each link. Data from extensive experiments are used to derive a multi-scale spatial weight model that is a function of the fade level, i.e., the difference between the predicted and measured RSS, and of the direction of RSS change. In addition, a measurement model is proposed which gives a probability of a person locating inside the derived spatial model for each given RSS measurement. A real-time radio tomographic imaging system is described which uses channel diversity and the presented models. Experiments in an open indoor environment, in a typical one-bedroom apartment and in a through-wall scenario are conducted to determine the accuracy of the system. We demonstrate that the new system is capable of localizing and tracking a person with high accuracy (<0.30 m) in all the environments, without the need to change the model parameters.
Year
Venue
Field
2013
CoRR
Tomographic reconstruction,Wireless,Spatial model,Device free localization,Simulation,Computer science,Communication channel,Real-time computing,Signal strength,Fade,RSS,Distributed computing
DocType
Volume
Citations 
Journal
abs/1302.5914
8
PageRank 
References 
Authors
0.71
17
3
Name
Order
Citations
PageRank
Ossi Kaltiokallio122114.41
Maurizio Bocca227416.62
Neal Patwari33805241.58