RF-Based Machine Learning Solution for Indoor Person Detection

Autor:	Juliano J. Bazzo, Thiago A. Scher, Alvaro Augusto Machado de Medeiros, Pedro Maia de Santana, Vicente A. de Sousa
Rok vydání:	2021
Předmět:	Person detection business.industry Computer science General Medicine Machine learning computer.software_genre Shannon information entropy Signal Standard deviation Zero (linguistics) Feature mapping Artificial intelligence Radio frequency business computer
Zdroj:	International Journal of Interdisciplinary Telecommunications and Networking. 13:42-50
ISSN:	1941-8671 1941-8663
DOI:	10.4018/ijitn.2021040104
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::bf693b2146dbbfb44a14f6d28bc4931f https://doi.org/10.4018/ijitn.2021040104 Zobrazit plný text záznamu