High-resolution hybrid TDM-CDM MIMO automotive radar
This paper proposes a deep learning (DL)-based high-resolution hybrid time-division multiplexing (TDM) and code-division multiplexing (CDM) multiple input multiple output (MIMO) automotive radar to enhance the discrimination capabilities of the radar in a cluttered environment. The hybrid TDM-CDM ap...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277267112500004X |
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| author | Zakaria Benyahia Mostafa Hefnawi Mohamed Aboulfatah Hassan Abdelmounim Jamal Zbitou |
| author_facet | Zakaria Benyahia Mostafa Hefnawi Mohamed Aboulfatah Hassan Abdelmounim Jamal Zbitou |
| author_sort | Zakaria Benyahia |
| collection | DOAJ |
| description | This paper proposes a deep learning (DL)-based high-resolution hybrid time-division multiplexing (TDM) and code-division multiplexing (CDM) multiple input multiple output (MIMO) automotive radar to enhance the discrimination capabilities of the radar in a cluttered environment. The hybrid TDM-CDM approach is implemented by partitioning the transmit and receive arrays into subarrays, applying CDM across the subarrays, while TDM is used within each subarray. On the other hand, the DL-based scheme utilizes the SqueezeNet deep convolutional neural network (DCNN), which treats the angle, range, and Doppler estimations of the extracted targets as a multi-label classification problem. Compared to CDM-MIMO radars, this approach requires fewer spreading codes, alleviating the challenge of spreading and despreading over each element. Compared to TDM-MIMO radars, it requires fewer time slots, increasing the refresh rate. Our approach outperforms existing DL-based TDM-MIMO radar systems and performs similarly to DL-based CDM-MIMO radar systems but with reduced complexity. Simulation results show that an angular resolution of 0.25° was achieved using 12-element transmit and receive arrays, each partitioned into three subarrays. |
| format | Article |
| id | doaj-art-baba98e12806499c918faaeb15d396fd |
| institution | Kabale University |
| issn | 2772-6711 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj-art-baba98e12806499c918faaeb15d396fd2025-01-30T05:15:16ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112025-03-0111100897High-resolution hybrid TDM-CDM MIMO automotive radarZakaria Benyahia0Mostafa Hefnawi1Mohamed Aboulfatah2Hassan Abdelmounim3Jamal Zbitou4Hassan 1st University FST Settat, Settat, Morocco; Corresponding author.Royal Military University of Canada Kingston, Ontario, CanadaHassan 1st University FST Settat, Settat, MoroccoHassan 1st University FST Settat, Settat, MoroccoAbdelmalek Essadi University Tétouan, Tétouan, MoroccoThis paper proposes a deep learning (DL)-based high-resolution hybrid time-division multiplexing (TDM) and code-division multiplexing (CDM) multiple input multiple output (MIMO) automotive radar to enhance the discrimination capabilities of the radar in a cluttered environment. The hybrid TDM-CDM approach is implemented by partitioning the transmit and receive arrays into subarrays, applying CDM across the subarrays, while TDM is used within each subarray. On the other hand, the DL-based scheme utilizes the SqueezeNet deep convolutional neural network (DCNN), which treats the angle, range, and Doppler estimations of the extracted targets as a multi-label classification problem. Compared to CDM-MIMO radars, this approach requires fewer spreading codes, alleviating the challenge of spreading and despreading over each element. Compared to TDM-MIMO radars, it requires fewer time slots, increasing the refresh rate. Our approach outperforms existing DL-based TDM-MIMO radar systems and performs similarly to DL-based CDM-MIMO radar systems but with reduced complexity. Simulation results show that an angular resolution of 0.25° was achieved using 12-element transmit and receive arrays, each partitioned into three subarrays.http://www.sciencedirect.com/science/article/pii/S277267112500004XAutomotive radarMIMO radarFMCW WaveformCDMTDMDeep learning |
| spellingShingle | Zakaria Benyahia Mostafa Hefnawi Mohamed Aboulfatah Hassan Abdelmounim Jamal Zbitou High-resolution hybrid TDM-CDM MIMO automotive radar e-Prime: Advances in Electrical Engineering, Electronics and Energy Automotive radar MIMO radar FMCW Waveform CDM TDM Deep learning |
| title | High-resolution hybrid TDM-CDM MIMO automotive radar |
| title_full | High-resolution hybrid TDM-CDM MIMO automotive radar |
| title_fullStr | High-resolution hybrid TDM-CDM MIMO automotive radar |
| title_full_unstemmed | High-resolution hybrid TDM-CDM MIMO automotive radar |
| title_short | High-resolution hybrid TDM-CDM MIMO automotive radar |
| title_sort | high resolution hybrid tdm cdm mimo automotive radar |
| topic | Automotive radar MIMO radar FMCW Waveform CDM TDM Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S277267112500004X |
| work_keys_str_mv | AT zakariabenyahia highresolutionhybridtdmcdmmimoautomotiveradar AT mostafahefnawi highresolutionhybridtdmcdmmimoautomotiveradar AT mohamedaboulfatah highresolutionhybridtdmcdmmimoautomotiveradar AT hassanabdelmounim highresolutionhybridtdmcdmmimoautomotiveradar AT jamalzbitou highresolutionhybridtdmcdmmimoautomotiveradar |