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MIMO Radar Systems: Deep Learning vs. Traditional Approaches
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Author(s): Mostafa Hefnawi (Royal Military College of Canada, Canada), Zakaria Benyahia (Hassan First University, Morocco), Mohamed Aboulfatah (Hassan First University, Morocco), Elhassane Abdelmounim (Hassan First University, Morocco)and Taoufiq Gadi (Hassan First University, Morocco)
Copyright: 2023
Pages: 25
Source title:
Handbook of Research on Emerging Designs and Applications for Microwave and Millimeter Wave Circuits
Source Author(s)/Editor(s): Jamal Zbitou (LABTIC, ENSA of Tangier, University of Abdelmalek Essaadi, Morocco), Mostafa Hefnawi (Royal Military College of Canada, Canada), Fouad Aytouna (LABTIC, ENSA of Tetouan, University of Abdelmalek Essaadi, Morocco)and Ahmed El Oualkadi (LabTIC, ENSA of Tangier, University of Abdelmalek Essaadi, Morocco)
DOI: 10.4018/978-1-6684-5955-3.ch010
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Abstract
Unlike traditional phased-array radars that need successive scans to cover the entire field of view, MIMO radar transmits orthogonal waveforms from each antenna element simultaneously, allowing the illumination of all targets at once. Also, better detection performance and a high spatial resolution can be obtained using all the components extracted by the matched filters. MIMO radar systems can detect the range, angle, and doppler of the targets, using traditional techniques such as the fast fourier transform (FFT), the multiple signal classifier (MUSIC), and the minimum variance distortionless response (MVDR). On the other hand, deep learning (DL) techniques have been proposed for MIMO radar systems as an alternative to traditional techniques that are computationally expensive and very sensitive to clutters and interferences. This chapter presents the performance of MIMO radar systems in a cluttered environment using both conventional and DL techniques.
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