DeFi: Robust Training-Free Device-Free Wireless Localization With WiFi

Many emerging applications, such as security surveillance and smart healthcare, have incubated the device-free localization (DFL) technique, which could estimate the location of a target without equipping any device. However, current DFL system needs labor-intensive training to learn the influence of the target on surrounding wireless signals, so as to provide model parameters or radio maps for the location estimation algorithm. To address this issue, we develop a novel robust training-free DFL system, named DeFi, which could directly estimate the target location by refining the angle-of-arrival (AOA) of the target reflection path based on WiFi channel state information. We analyze the characteristics of the static paths and the motion paths, eliminate the static paths using a background elimination algorithm, and separate out the target reflection path from the motion paths according to the AOA and equivalent time-of-flight measurements. With the AOA of the target reflection path as observation information, we robustly estimate the target location based on a particle filter algorithm. We implement the proposed DeFi system on commodity off-the-shelf WiFi devices and validate its performance in two indoor scenarios. Experimental results show that DeFi achieves a median error of less than 0.6 m.