PhD

dissertationDevice-free localization (DFL) is the practice of locating people or objects when no tag or device is attached to the entity being tracked. DFL technologies are useful in applications where the targets being tracked and detected are not expected to cooperate with the system. This may be the case because the entities being tracked are evading surveillance, because they are unable, or because they do not want to be inconvenienced. This dissertation discusses some novel and cost-effective methods for locating people with received signal strength (RSS) measurements in wireless networks. The first contribution of this work presents a linear model for using received signal strength (RSS) measurements to obtain images of moving objects, a process called radio tomographic imaging (RTI). Noise models are investigated based on real measurements of a deployed RTI system. Mean-squared error (MSE) bounds on image accuracy are derived, which are used to calculate the accuracy of an RTI system for a given node geometry. The ill-posedness of RTI is discussed, and Tikhonov regularization is used to derive an image estimator. We then present variance-based RTI, which takes advantage of the motioninduced variance of received signal strength measurements. 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. The final contribution of this dissertation introduces measurement-based statistical models that can be used to estimate the locations of people using signal strength measurements in wireless networks. We demonstrate, using extensive experimental data, that changes in signal strength measurements due to human motion can be modeled by the skew-Laplace distribution. The parameters of the distribution are dependent on the position of person and on the amount of fading that a particular link experiences. Using the skew-Laplace likelihood model, we apply a particle filter to experimentally estimate the location of moving and stationary people through walls