Material Identification Using RF Sensors and Convolutional Neural Networks

Gianluca Agresti; Simone Milani

ICASSP 2019accepted0 citations

Material Identification Using RF Sensors and Convolutional Neural Networks

Abstract

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.

BibTeX

@inproceedings{icassp2019_materialidentifi,
  title = {Material Identification Using RF Sensors and Convolutional Neural Networks},
  author = {Gianluca Agresti and Simone Milani},
  booktitle = {ICASSP 2019},
  year = {2019}
}