  RF sensor networks are 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 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-of-the-art RTI methods.
