A Comparison of Processing Schemes for Automotive MIMO SAR Imaging
Synthetic Aperture Radar (SAR) imaging is starting to play an essential role in the automotive industry. Its day and night sensing capability, fine resolution, and high flexibility are key aspects making SAR a very compelling instrument in this field. This paper describes and compares three algorith...
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MDPI AG
2022-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/19/4696 |
| _version_ | 1827653327295348736 |
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| author | Marco Manzoni Stefano Tebaldini Andrea Virgilio Monti-Guarnieri Claudio Maria Prati Ivan Russo |
| author_facet | Marco Manzoni Stefano Tebaldini Andrea Virgilio Monti-Guarnieri Claudio Maria Prati Ivan Russo |
| author_sort | Marco Manzoni |
| collection | DOAJ |
| description | Synthetic Aperture Radar (SAR) imaging is starting to play an essential role in the automotive industry. Its day and night sensing capability, fine resolution, and high flexibility are key aspects making SAR a very compelling instrument in this field. This paper describes and compares three algorithms used to combine low-resolution images acquired by a Multiple-Input Multiple-Output (MIMO) automotive radar to form an SAR image of the environment. The first is the well-known Fast Factorized Back-Projection (FFBP), which focuses the image in different stages. The second one will be called 3D2D, and it is a simple 3D interpolation used to extract the SAR image from the Range-Angle-Velocity (RAV) data cube. The third will be called Quick&Dirty (Q&D), and it is a fast alternative to the 3D2D scheme that exploits the same intuition. A rigorous mathematical description of each algorithm is derived, and their limits are addressed. We then provide simulated results assessing different interpolation kernels, proving which one performs better. A rough estimation of the number of operations proves that both algorithms can be deployed using a real-time implementation. Finally, we will present some experimental results based on open road campaign data acquired using an eight-channel MIMO radar at 77 GHz, considering the case of a forward-looking geometry. |
| first_indexed | 2024-03-09T21:15:26Z |
| format | Article |
| id | doaj.art-e96e64967c3f41a8b1e7a6db4f8ae9c8 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:15:26Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e96e64967c3f41a8b1e7a6db4f8ae9c82023-11-23T21:37:03ZengMDPI AGRemote Sensing2072-42922022-09-011419469610.3390/rs14194696A Comparison of Processing Schemes for Automotive MIMO SAR ImagingMarco Manzoni0Stefano Tebaldini1Andrea Virgilio Monti-Guarnieri2Claudio Maria Prati3Ivan Russo4Dipartimento di Elettronica Informazione e Bioingegneria (DEIB), Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, ItalyDipartimento di Elettronica Informazione e Bioingegneria (DEIB), Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, ItalyDipartimento di Elettronica Informazione e Bioingegneria (DEIB), Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, ItalyDipartimento di Elettronica Informazione e Bioingegneria (DEIB), Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, ItalyHuawei Technologies Italia S.r.l., 20129 Milano, ItalySynthetic Aperture Radar (SAR) imaging is starting to play an essential role in the automotive industry. Its day and night sensing capability, fine resolution, and high flexibility are key aspects making SAR a very compelling instrument in this field. This paper describes and compares three algorithms used to combine low-resolution images acquired by a Multiple-Input Multiple-Output (MIMO) automotive radar to form an SAR image of the environment. The first is the well-known Fast Factorized Back-Projection (FFBP), which focuses the image in different stages. The second one will be called 3D2D, and it is a simple 3D interpolation used to extract the SAR image from the Range-Angle-Velocity (RAV) data cube. The third will be called Quick&Dirty (Q&D), and it is a fast alternative to the 3D2D scheme that exploits the same intuition. A rigorous mathematical description of each algorithm is derived, and their limits are addressed. We then provide simulated results assessing different interpolation kernels, proving which one performs better. A rough estimation of the number of operations proves that both algorithms can be deployed using a real-time implementation. Finally, we will present some experimental results based on open road campaign data acquired using an eight-channel MIMO radar at 77 GHz, considering the case of a forward-looking geometry.https://www.mdpi.com/2072-4292/14/19/4696radarSARautomotivefocusingFFBPcar-based |
| spellingShingle | Marco Manzoni Stefano Tebaldini Andrea Virgilio Monti-Guarnieri Claudio Maria Prati Ivan Russo A Comparison of Processing Schemes for Automotive MIMO SAR Imaging Remote Sensing radar SAR automotive focusing FFBP car-based |
| title | A Comparison of Processing Schemes for Automotive MIMO SAR Imaging |
| title_full | A Comparison of Processing Schemes for Automotive MIMO SAR Imaging |
| title_fullStr | A Comparison of Processing Schemes for Automotive MIMO SAR Imaging |
| title_full_unstemmed | A Comparison of Processing Schemes for Automotive MIMO SAR Imaging |
| title_short | A Comparison of Processing Schemes for Automotive MIMO SAR Imaging |
| title_sort | comparison of processing schemes for automotive mimo sar imaging |
| topic | radar SAR automotive focusing FFBP car-based |
| url | https://www.mdpi.com/2072-4292/14/19/4696 |
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