LEVERAGING PROCESSOR ELECTROMAGNETIC RADIATION FOR RADIO TOMOGRAPHY

Abstract

Abstract Radio Tomographic Imaging (RTI) is a promising technique for imaging non line of sight areas, but current implementations require complex setups with multiple, symmetrically placed transceivers and costly, dedicated radio circuits. These systems are difficult to be used in embedded and IoT applications and are highly sensitive to variations in terrain and surface structure, which can significantly impact signal measurements. This research investigates an approach that leverages unintentional Electromagnetic (EM) radiation emitted by processors within computing devices. The results demonstrate that an object in Line of Sight (LOS) between the signal source and receiver causes measurable variations in the received signal from 0.65 to 3.52 dBm. But this value highly depends on the area of interest and surrounding. According to the results, changes are detectable up to 2.4 m from the signal source in a cluttered indoor environment and up to 3 m in an open outdoor environment. Moreover, using signal propagation characteristics of a single link, the location of an object in the LOS can be estimated up to 0.1072 m mean absolute error with two possible locations which can be reduced to one correct estimation with particle filters like Kalman filter. To date, most efforts in the RTI field have focused on developing algorithms that enhance tomographic image accuracy. However, there is a noticeable gap in work addressing computational efficiency and practical feasibility. Building on the observed characteristics of unintentional EM radiation from the signal source, this research proposes a novel weight model algorithm designed to reduce computational cost compared to the statistical tomographic imaging model introduced by Wilson and Patwari in “Radio Tomographic Imaging with Wireless Networks”.