  This paper presents a new method for imaging, localizing, and tracking motion
behind walls in real-time. The method takes advantage of the motion-induced
variance of received signal strength measurements made in a wireless
peer-to-peer network. Using a multipath channel model, we show that the signal
strength on a wireless link is largely dependent on the power contained in
multipath components that travel through space containing moving objects. A
statistical model relating variance to spatial locations of movement is
presented and used as a framework for the estimation of a motion image. From
the motion image, the Kalman filter is applied to recursively track the
coordinates of a moving target. Experimental results for a 34-node through-wall
imaging and tracking system over a 780 square foot area are presented.
