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      Doppler-Spectrum Feature-Based Human–Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor

      research-article
      * ,
      Sensors (Basel, Switzerland)
      MDPI
      human detection, FMCW radar, range-Doppler processing, radar machine learning

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          Abstract

          In this paper, we propose a Doppler-spectrum feature-based human–vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering point difference, and magnitude difference rate features based on the characteristics of the Doppler spectrum in two successive frames. We also use an SVM (support vector machine) and BDT (binary decision tree) for training and validation of the three aforementioned features. We measured the signals using a 24-GHz FMCW radar front-end module and a real-time data acquisition module and extracted three features from a walking human and a moving vehicle in the field. We then repeatedly measured the classification decision rate of the proposed algorithm using the SVM and BDT, finding that the average performance exceeded 99% and 96% for the walking human and the moving vehicle, respectively.

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          Most cited references27

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          Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine

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            Human Detection Using Doppler Radar Based on Physical Characteristics of Targets

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              • Record: found
              • Abstract: not found
              • Article: not found

              Personnel Recognition and Gait Classification Based on Multistatic Micro-Doppler Signatures Using Deep Convolutional Neural Networks

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                02 April 2020
                April 2020
                : 20
                : 7
                : 2001
                Affiliations
                Division of Automotive Technology, ICT Research Institute, Convergence Research Institute, DGIST, 333, Techno Jungang-daero 333, Hyeonpung-myeon, Dalseong-gun, Daegu 42988, Korea; ysjin@ 123456dgist.ac.kr
                Author notes
                [* ]Correspondence: braham@ 123456dgist.ac.kr ; Tel.: +82-53-785-4560
                Author information
                https://orcid.org/0000-0001-8196-7173
                Article
                sensors-20-02001
                10.3390/s20072001
                7180962
                32252496
                b6bfce53-0670-405c-9c9f-43509bf1cd94
                © 2020 by the authors.

                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 ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 28 February 2020
                : 31 March 2020
                Categories
                Article

                Biomedical engineering
                human detection,fmcw radar,range-doppler processing,radar machine learning

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