RF-Driven Crowd-Size Classifcation via Machine Learning

被引：1

作者：

de Brito Guerra, Tarciana Cabral ^{[1
]}

de Santana, Pedro Maia ^{[2
]}

de Medeiros Campos, Millena Michely ^{[1
]}

Mattos, Mateus de Oliveira ^{[3
]}

de Medeiros, Alvaro A. M. ^{[4
]}

de Sousa, Vicente Angelo ^{[1
]}

机构：

[1] Univ Fed Rio Grande do Norte, BR-59078970 Natal, RN, Brazil

[2] Samsung R&D Inst SIDTA, BR-69030060 Manaus, Amazonas, Brazil

[3] Ctr Res & Dev Telecommun CPqD, BR-13086902 Campinas, SP, Brazil

[4] Univ Fed Juiz de Fora, BR-36036900 Juiz De Fora, Brazil

来源：

IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS | 2019年 / 18卷 / 11期

关键词：

Indoor people detection; machine learning (ML); radio frequency (RF) signal;

D O I：

10.1109/LAWP.2019.2932076

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：2321 / 2324

页数：4

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