A Two-Stage Support Vector Machine and SqueezeNet System for Range-Angle and Range-Speed Estimation in a Cluttered Environment of Automotive MIMO Radar Systems
This paper proposes a two-stage deep-learning approach for frequency modulated continuous waveform multiple‐input multiple‐output (FMCW MIMO) radar embedded in cluttered and jammed environments. The first stage uses the support vector machine (SVM) as a feature extractor that discriminates targets f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2022-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2022/08/itmconf_iccwcs2022_01010.pdf |
| _version_ | 1811258515981860864 |
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| author | Benyahia Zakaria Hefnawi Mostafa Aboulfatah Mohamed Abdelmounim Hassan Gadi Taoufiq |
| author_facet | Benyahia Zakaria Hefnawi Mostafa Aboulfatah Mohamed Abdelmounim Hassan Gadi Taoufiq |
| author_sort | Benyahia Zakaria |
| collection | DOAJ |
| description | This paper proposes a two-stage deep-learning approach for frequency modulated continuous waveform multiple‐input multiple‐output (FMCW MIMO) radar embedded in cluttered and jammed environments. The first stage uses the support vector machine (SVM) as a feature extractor that discriminates targets from clutters and jammers. In the second stage, the angle, range, and Doppler estimations of the extracted targets are treated by the SqueezeNet deep convolutional neural network (DCNN) as a multilabel classification problem. The performance of the proposed hybrid SVM-SqueezeNet method is very close to the one achieved by the SqueezeNet only but with the advantage of identifying the type of targets and reducing the training time required by the SqueezeNet. |
| first_indexed | 2024-04-12T18:14:53Z |
| format | Article |
| id | doaj.art-5b35d3e9d1714a9b855ce7bce9d2488c |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-04-12T18:14:53Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-5b35d3e9d1714a9b855ce7bce9d2488c2022-12-22T03:21:40ZengEDP SciencesITM Web of Conferences2271-20972022-01-01480101010.1051/itmconf/20224801010itmconf_iccwcs2022_01010A Two-Stage Support Vector Machine and SqueezeNet System for Range-Angle and Range-Speed Estimation in a Cluttered Environment of Automotive MIMO Radar SystemsBenyahia Zakaria0Hefnawi Mostafa1Aboulfatah Mohamed2Abdelmounim Hassan3Gadi Taoufiq4Hassan I University SettatRoyal Military College of CanadaHassan I University SettatHassan I University SettatHassan I University SettatThis paper proposes a two-stage deep-learning approach for frequency modulated continuous waveform multiple‐input multiple‐output (FMCW MIMO) radar embedded in cluttered and jammed environments. The first stage uses the support vector machine (SVM) as a feature extractor that discriminates targets from clutters and jammers. In the second stage, the angle, range, and Doppler estimations of the extracted targets are treated by the SqueezeNet deep convolutional neural network (DCNN) as a multilabel classification problem. The performance of the proposed hybrid SVM-SqueezeNet method is very close to the one achieved by the SqueezeNet only but with the advantage of identifying the type of targets and reducing the training time required by the SqueezeNet.https://www.itm-conferences.org/articles/itmconf/pdf/2022/08/itmconf_iccwcs2022_01010.pdf |
| spellingShingle | Benyahia Zakaria Hefnawi Mostafa Aboulfatah Mohamed Abdelmounim Hassan Gadi Taoufiq A Two-Stage Support Vector Machine and SqueezeNet System for Range-Angle and Range-Speed Estimation in a Cluttered Environment of Automotive MIMO Radar Systems ITM Web of Conferences |
| title | A Two-Stage Support Vector Machine and SqueezeNet System for Range-Angle and Range-Speed Estimation in a Cluttered Environment of Automotive MIMO Radar Systems |
| title_full | A Two-Stage Support Vector Machine and SqueezeNet System for Range-Angle and Range-Speed Estimation in a Cluttered Environment of Automotive MIMO Radar Systems |
| title_fullStr | A Two-Stage Support Vector Machine and SqueezeNet System for Range-Angle and Range-Speed Estimation in a Cluttered Environment of Automotive MIMO Radar Systems |
| title_full_unstemmed | A Two-Stage Support Vector Machine and SqueezeNet System for Range-Angle and Range-Speed Estimation in a Cluttered Environment of Automotive MIMO Radar Systems |
| title_short | A Two-Stage Support Vector Machine and SqueezeNet System for Range-Angle and Range-Speed Estimation in a Cluttered Environment of Automotive MIMO Radar Systems |
| title_sort | two stage support vector machine and squeezenet system for range angle and range speed estimation in a cluttered environment of automotive mimo radar systems |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2022/08/itmconf_iccwcs2022_01010.pdf |
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