A Novel Bayesian Filter for RSS-Based Device-Free Localization and Tracking

Received signal strength based device-free localization applications utilize a model that relates the measurements to position of the wireless sensors and person, and the underlying inverse problem is solved either using an imaging method or a nonlinear Bayesian filter. In this paper, it is shown that the Bayesian filters nearly reach the posterior Cramér-Rao bound and they are superior with respect to imaging approaches in terms of localization accuracy because the measurements are directly related to position of the person. However, Bayesian filters are known to suffer from divergence issues and in this paper, the problem is addressed by introducing a novel Bayesian filter. The developed filter augments the measurement model of a Bayesian filter with position estimates from an imaging approach. This bounds the filter's measurement residuals by the position errors of the imaging approach and as an outcome, the developed filter has robustness of an imaging method and tracking accuracy of a Bayesian filter. The filter is demonstrated to achieve a localization error of 0.11 \text{ m}0.11m in a 75 \; \text{m}^275m2 open indoor deployment and an error of 0.29 \text{ m}0.29m in a 82 \; \text{m}^282m2 apartment experiment, decreasing the localization error by 30-48 percent with respect to a state-of-the-art imaging method.