Modelling of the floor effects in device-free radio localization applications

Device-free localization (DFL) systems exploit the human-induced perturbations of the electromagnetic (EM) fields as a privacy-preserving sensing tool for passive detection, recognition, localization, and tracking. Without wearing any electronic device, the monitored subjects (targets) modify the EM field (e.g., the Received Signal Strength - RSS) in a way that depends on their location relative to the wireless devices. Thus, DFL systems exploit specific radio maps to reconstruct the body-induced alterations of the EM field and enable motion tracking. These maps can be learned from training data or obtained from a physical/EM model. Practical EM models are based on the scalar diffraction theory and predict the impact of subject motions on the radio propagation without requiring time-consuming computations. However, they are often limited by free-space propagation assumptions that are unsuitable for complex environments characterized by significant multipath effects. This paper discusses and extends the generic diffraction-based models by considering also the floor influence in indoor scenarios. The proposed model is validated by EM simulations and experiments. The impact of this model on the statistical characterization of the RSS is also analyzed for selected target locations.