@ARTICLE{boracchi15_TMC, 
author={C. Alippi and M. Bocca and G. Boracchi and N. Patwari and M. Roveri}, 
journal={IEEE Transactions on Mobile Computing}, 
title={RTI Goes Wild: Radio Tomographic Imaging for Outdoor People Detection and Localization}, 
year={2015}, 
volume={PP}, 
number={99}, 
pages={1-1}, 
abstract={In recent years, Radio frequency (RF) sensor networks have been used to localize people indoor without requiring them to wear invasive electronic devices. These wireless mesh networks, formed by low-power radio transceivers, continuously measure the received signal strength (RSS) of the links. Radio Tomographic Imaging (RTI) is a technique that generates, starting from these RSS measurements, 2D images of the change in the electromagnetic field inside the area covered by the radio transceivers to spot the presence and movements of animates (e.g., people, large animals) or large metallic objects (e.g., cars). Here, we present a RTI system for localizing and tracking people outdoors. Differently than in indoor environments where the RSS does not change significantly with time unless people are found in the monitored area, the outdoor RSS signal is time-variant, e.g., due to rainfalls or wind-driven foliage. We present a novel outdoor RTI method that, despite the nonstationary noise introduced in the RSS data by the environment, achieves high localization accuracy and dramatically reduces the energy consumption of the sensing units. Experimental results demonstrate that the system accurately detects and tracks a person in real-time in a large forested area under varying environmental conditions, significantly reducing false positives, localization error and energy consumption compared to state-ofthe- art RTI methods.}, 
keywords={Animals;Cameras;Indoor environments;Monitoring;Radio frequency;Tomography;Working environment noise;adaptive systems;device-free localization;radio tomography;wireless sensor networks}, 
doi={10.1109/TMC.2015.2504965}, 
ISSN={1536-1233}, 
month={},}