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MATERIAL IDENTIFICATION USING RF SENSORS AND CONVOLUTIONAL NEURAL NETWORKS

Citation Author(s):
Gianluca Agresti, Simone Milani
Submitted by:
Gianluca Agresti
Last updated:
8 May 2019 - 9:34am
Document Type:
Poster
Document Year:
2019
Event:
Presenters:
Simone MIlani
Paper Code:
4700
 

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.

https://ieeexplore.ieee.org/abstract/document/8682296

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