Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers
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| Format: | research article |
| Language: | English |
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IEEE
2023
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| Online Access: | https://cris.library.msu.ac.zw//handle/11408/5496 |
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| author | Harry D. Mafukidze Amit Kumar Mishra Jan Pidanic |
| author2 | Department of Applied Physics and Telecommunications Faculty of Science and Technology Midlands State University, Gweru, Zimbabwe |
| author_facet | Department of Applied Physics and Telecommunications Faculty of Science and Technology Midlands State University, Gweru, Zimbabwe Harry D. Mafukidze Amit Kumar Mishra Jan Pidanic |
| author_sort | Harry D. Mafukidze |
| collection | DSpace |
| description | Abstract |
| format | research article |
| id | ir-11408-5496 |
| institution | My University |
| language | English |
| publishDate | 2023 |
| publisher | IEEE |
| record_format | dspace |
| spelling | ir-11408-54962023-03-29T06:55:29Z Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers Harry D. Mafukidze Amit Kumar Mishra Jan Pidanic Department of Applied Physics and Telecommunications Faculty of Science and Technology Midlands State University, Gweru, Zimbabwe Department of Electrical Engineering University of Cape Town Cape Town, South Africa Department of Electrical Engineering Faculty of Electrical Engineering and Informatics University of Pardubice, Pardubice, Czech Republic mmWave radar single-chip radar radar point clouds scattering centers COVNETs Abstract Recently, mmWave radars have been gaining popularity, thanks to their low cost, ease of use and high-resolution sensing. In this paper, we provide a review of the mmWave radar data processing frameworks, starting from mathematical foundations to applications. Specifically, we focus on the mmWave radar point cloud as a robust data structure representing compressed signatures for target recognition and classification. We first focus on the generation of the radar point clouds, and the signal processing algorithms designed for their unique characteristics. Then, we illustrate how the radar point clouds are prepared for feature extraction and classification using machine learning and deep learning approaches. Finally, we summarize the state-of-the-art applications, open datasets, developments and future research directions in this field. 10 110992 111021 2023-03-29T06:55:28Z 2023-03-29T06:55:28Z 2022-10-03 research article https://cris.library.msu.ac.zw//handle/11408/5496 DOI: 10.1109/ACCESS.2022.3211673 en IEEE Access 2169-3536 open IEEE |
| spellingShingle | mmWave radar single-chip radar radar point clouds scattering centers COVNETs Harry D. Mafukidze Amit Kumar Mishra Jan Pidanic Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers |
| title | Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers |
| title_full | Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers |
| title_fullStr | Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers |
| title_full_unstemmed | Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers |
| title_short | Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers |
| title_sort | scattering centers to point clouds: a review of mmwave radars for non-radar-engineers |
| topic | mmWave radar single-chip radar radar point clouds scattering centers COVNETs |
| url | https://cris.library.msu.ac.zw//handle/11408/5496 |
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