Sundew: Design and Evaluation of a Model-Based Device-Free Localization System

The state-of-the-art in device-free localization systems based on RF-measurements is fingerprinting. Fingerprinting requires reference measurements called fingerprints that are recorded during a training phase. Especially in device-free localization systems, recording of reference measurements for fingerprinting is a tedious, costly, and error-prone task. In this paper, we propose Sundew, a model-based device-free localization system that does not need fingerprinting in the sense of reference measurements but is able to calculate signal strength values at any position and compare it to actual measurements after a simple calibration phase. Sundew - as any device-free localization system - requires a metric for comparison of feature vectors. In this paper, we investigate the influence of nine different distance metrics on the positioning accuracy. Simulations and measurements show that our suggested model-based device-free localization system works best with the $L_{1}$ distance metric. Sundew estimates 90 % of positions in a $\mathbf{2.5\ mx\ 2.5m}$ grid correctly, independent of the orientation of the person in the target area.