RF-Driven Crowd-Size Classif i cation via Machine Learning

In this letter, we propose a machine learning solution for crowd-size classification in an indoor environment. Narrow-band radio frequency signals are used to identify a pattern according to the number of people. Experimental data collected by a low-cost software-defined radio platform are postproce...

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Veröffentlicht in:	IEEE antennas and wireless propagation letters 2019-01, Vol.18 (11), p.2321
Hauptverfasser:	Tarciana Cabral de Brito Guerra, Maia de Santana, Pedro, Millena Michely de Medeiros Campos, Mateus de Oliveira Mattos, Alvaro A M de Medeiros, de Sousa, Vicente Angelo
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial intelligence Classification Indoor environments Machine learning Mapping Radio signals Software radio
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creator	Tarciana Cabral de Brito Guerra Maia de Santana, Pedro Millena Michely de Medeiros Campos Mateus de Oliveira Mattos Alvaro A M de Medeiros de Sousa, Vicente Angelo
description	In this letter, we propose a machine learning solution for crowd-size classification in an indoor environment. Narrow-band radio frequency signals are used to identify a pattern according to the number of people. Experimental data collected by a low-cost software-defined radio platform are postprocessed by applying a feature mapping along with the random forest technique for classifying the crowd-size scenarios. The proposed solution has significant accuracy in classification performance.
doi_str_mv	10.1109/LAWP.2019.2932076
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subjects	Artificial intelligence Classification Indoor environments Machine learning Mapping Radio signals Software radio
title	RF-Driven Crowd-Size Classif i cation via Machine Learning
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