RF-Based Machine Learning Solution for Indoor Person Detection

Pedro Maia De Santana, Thiago A. Scher, Juliano Joao Bazzo, Alvaro A. M. de Medeiros and Vicente A. de Sousa
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Pedro Maia De Santana: Samsung SIDIA, Brazil
Thiago A. Scher: Samsung SIDIA, Brazil
Juliano Joao Bazzo: Samsung SIDIA, Brazil
Alvaro A. M. de Medeiros: Federal University of Juiz de Fora, Brazil
Vicente A. de Sousa: Federal University of Rio Grande do Norte, Brazil

International Journal of Interdisciplinary Telecommunications and Networking (IJITN), 2021, vol. 13, issue 2, 42-50

Abstract: Machine learning techniques applied to radio frequency (RF) signals are used for many applications in addition to data communication. In this paper, the authors propose a machine learning solution for classifying the number of people within an indoor ambient. The main idea is to identify a pattern of received signal characteristics according to the number of people. Experimental measurements are performed using a software-defined radio platform inside a laboratory. The data collected is post-processed by applying a feature mapping technique based on mean, standard deviation, and Shannon information entropy. This feature-space data is then used to train a supervised machine learning network for classifying scenarios with zero, one, two, and three people inside. The proposed solution presents significant accuracy in classification performance.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jitn00:v:13:y:2021:i:2:p:42-50

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