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Machine Learning Based Object Classification and Identification Scheme Using an Embedded Millimeter-Wave Radar Sensor

A target’s movements and radar cross sections are the key parameters to consider when designing a radar sensor for a given application. This paper shows the feasibility and effectiveness of using 24 GHz radar built-in low-noise microwave amplifiers for detecting an object. For this purpose a supervi...

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Detalles Bibliográficos
Autores principales: Arab, Homa, Ghaffari, Iman, Chioukh, Lydia, Tatu, Serioja, Dufour, Steven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272183/
https://www.ncbi.nlm.nih.gov/pubmed/34201765
http://dx.doi.org/10.3390/s21134291
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author Arab, Homa
Ghaffari, Iman
Chioukh, Lydia
Tatu, Serioja
Dufour, Steven
author_facet Arab, Homa
Ghaffari, Iman
Chioukh, Lydia
Tatu, Serioja
Dufour, Steven
author_sort Arab, Homa
collection PubMed
description A target’s movements and radar cross sections are the key parameters to consider when designing a radar sensor for a given application. This paper shows the feasibility and effectiveness of using 24 GHz radar built-in low-noise microwave amplifiers for detecting an object. For this purpose a supervised machine learning model (SVM) is trained using the recorded data to classify the targets based on their cross sections into four categories. The trained classifiers were used to classify the objects with varying distances from the receiver. The SVM classification is also compared with three methods based on binary classification: a one-against-all classification, a one-against-one classification, and a directed acyclic graph SVM. The level of accuracy is approximately 96.6%, and an F1-score of 96.5% is achieved using the one-against-one SVM method with an RFB kernel. The proposed contactless radar in combination with an SVM algorithm can be used to detect and categorize a target in real time without a signal processing toolbox.
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spelling pubmed-82721832021-07-11 Machine Learning Based Object Classification and Identification Scheme Using an Embedded Millimeter-Wave Radar Sensor Arab, Homa Ghaffari, Iman Chioukh, Lydia Tatu, Serioja Dufour, Steven Sensors (Basel) Article A target’s movements and radar cross sections are the key parameters to consider when designing a radar sensor for a given application. This paper shows the feasibility and effectiveness of using 24 GHz radar built-in low-noise microwave amplifiers for detecting an object. For this purpose a supervised machine learning model (SVM) is trained using the recorded data to classify the targets based on their cross sections into four categories. The trained classifiers were used to classify the objects with varying distances from the receiver. The SVM classification is also compared with three methods based on binary classification: a one-against-all classification, a one-against-one classification, and a directed acyclic graph SVM. The level of accuracy is approximately 96.6%, and an F1-score of 96.5% is achieved using the one-against-one SVM method with an RFB kernel. The proposed contactless radar in combination with an SVM algorithm can be used to detect and categorize a target in real time without a signal processing toolbox. MDPI 2021-06-23 /pmc/articles/PMC8272183/ /pubmed/34201765 http://dx.doi.org/10.3390/s21134291 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Arab, Homa
Ghaffari, Iman
Chioukh, Lydia
Tatu, Serioja
Dufour, Steven
Machine Learning Based Object Classification and Identification Scheme Using an Embedded Millimeter-Wave Radar Sensor
title Machine Learning Based Object Classification and Identification Scheme Using an Embedded Millimeter-Wave Radar Sensor
title_full Machine Learning Based Object Classification and Identification Scheme Using an Embedded Millimeter-Wave Radar Sensor
title_fullStr Machine Learning Based Object Classification and Identification Scheme Using an Embedded Millimeter-Wave Radar Sensor
title_full_unstemmed Machine Learning Based Object Classification and Identification Scheme Using an Embedded Millimeter-Wave Radar Sensor
title_short Machine Learning Based Object Classification and Identification Scheme Using an Embedded Millimeter-Wave Radar Sensor
title_sort machine learning based object classification and identification scheme using an embedded millimeter-wave radar sensor
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272183/
https://www.ncbi.nlm.nih.gov/pubmed/34201765
http://dx.doi.org/10.3390/s21134291
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