Material Identification Using RF Sensors and Convolutional Neural Networks

Recent years have assisted a widespreading of Radio-Frequency-based tracking and mapping algorithms for a wide range of applications, ranging from environment surveillance to human-computer interface. This work presents a material identification system based on a portable 3D imaging radar-based system, the Walabot sensor by Vayyar Technologies; the acquired three-dimensional radiance map of the analyzed object is processed by a Convolutional Neural Network in order to identify which material the object is made of. Experimental results show that processing the three-dimensional radiance volume proves to be more efficient thas processing the raw signals from antennas. Moreover, the proposed solution presents a higher accuracy with respect to some previous state-of-the-art solutions.