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RF-Based Machine Learning Solution for Indoor Person Detection

RF-Based Machine Learning Solution for Indoor Person Detection

Pedro Maia De Santana, Thiago A. Scher, Juliano Joao Bazzo, Alvaro A. M. de Medeiros, Vicente A. de Sousa Jr.
Copyright: © 2021 |Volume: 13 |Issue: 2 |Pages: 9
ISSN: 1941-8663|EISSN: 1941-8671|EISBN13: 9781799860570|DOI: 10.4018/IJITN.2021040104
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MLA

De Santana, Pedro Maia, et al. "RF-Based Machine Learning Solution for Indoor Person Detection." IJITN vol.13, no.2 2021: pp.42-50. http://doi.org/10.4018/IJITN.2021040104

APA

De Santana, P. M., Scher, T. A., Bazzo, J. J., de Medeiros, A. A., & de Sousa Jr., V. A. (2021). RF-Based Machine Learning Solution for Indoor Person Detection. International Journal of Interdisciplinary Telecommunications and Networking (IJITN), 13(2), 42-50. http://doi.org/10.4018/IJITN.2021040104

Chicago

De Santana, Pedro Maia, et al. "RF-Based Machine Learning Solution for Indoor Person Detection," International Journal of Interdisciplinary Telecommunications and Networking (IJITN) 13, no.2: 42-50. http://doi.org/10.4018/IJITN.2021040104

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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.

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