Blind Radio Tomography

From the attenuation measurements collected by a network of spatially distributed sensors, radio tomography constructs spatial loss fields (SLFs) that quantify absorption of radiofrequency waves at each location. These SLFs can be used for interference prediction in (possibly cognitive) wireless communication networks, for environmental monitoring or intrusion detection in surveillance applications, for through-the-wall imaging, for survivor localization after earthquakes or fires, etc. The cornerstone of radio tomography is to model attenuation as the bidimensional integral of the SLF of interest scaled by a weight function. Unfortunately, existing approaches (i) rely on heuristic assumptions to select the weight function and (ii) are limited to imaging changes in the propagation medium or they require a separate calibration step with measurements in free space. The first major contribution in this paper addresses (i) by means of a blind radio tomographic approach that learns the SLF together with the aforementioned weight function from the attenuation measurements. This challenging problem is tackled by capitalizing on contemporary kernel-based learning tools together with various forms of regularization that leverage prior knowledge. The second contribution addresses (ii) by means of a novel calibration technique capable of imaging static structures without separate calibration steps. Numerical tests with real and synthetic measurements validate the efficacy of the proposed algorithms.