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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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-06, Vol.21 (13), p.4291
Main Authors: Arab, Homa, Ghaffari, Iman, Chioukh, Lydia, Tatu, Serioja, Dufour, Steven
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
doppler frequency
in-phase/quadrature demodulator
Machine learning
metronome
Microwave amplifiers
Millimeter waves
millimeter-wave
multi-class SVMs
Neural networks
Noise
Radar cross sections
Sensors
Signal processing
Skin cancer
Support vector machines
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21134291