Through-Wall Tracking With Radio Tomography Networks Using Foreground Detection
This paper presents a novel method for tracking a moving person or object through walls using wireless networks. The method takes advantage of the motion-induced variation of Received Signal Strength (RSS) measurements in a radio tomography network. Based on real measurements of a deployed network, the authors show that the RSS distribution on a wireless link can be modeled as a mixture of Gaussians. An online learning algorithm is then proposed to update the model and detect whether the link is affected by the motion. Using spatial locations of the affected links, they apply the sequential Monte Carlo (SMC) methods to track the coordinates of a moving target.