Abstract
Employing deep learning methodologies for computer vision tasks, particularly in the domain of radar image analysis, necessitates access to a large and diverse dataset. In the context of radar imagery, the creation of such a dataset often entails the intricate task of reconstructing images from raw radar back-scattered data. This reconstruction process involves handling substantial data volumes, which can be computationally intensve and time-consuming. In this research, a deep learning framework is proposed for target classification utilizing solely the radar back-scattered data, completely bypassing the need for image reconstruction procedure, thereby significantly reducing the classification time. To make the dataset generation easier, a computational imaging (CI) numerical model is employed. Subsequently, the deep learning model is trained using this dataset, and following the training phase, it is tested with radar back-scattered data that is not included in the network training. The outcomes of this evaluation confirm the benefit of training a deep learning model to perform image identification tasks based on radar back-scattered signatures.
Original language | English |
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Title of host publication | 2024 IEEE European Conference on Antennas and Propagation (EuCAP): proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9788831299091 |
ISBN (Print) | 9798350394436 |
DOIs | |
Publication status | Published - 26 Apr 2024 |
Event | 18th European Conference on Antennas and Propagation 2024 - Glasgow, United Kingdom Duration: 17 Mar 2024 → 22 Mar 2024 https://www.eucap2024.org/ |
Publication series
Name | European Conference on Antennas and Propagation (EUCAP): proceedings |
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ISSN (Print) | 2164-3342 |
Conference
Conference | 18th European Conference on Antennas and Propagation 2024 |
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Abbreviated title | EuCAP 2024 |
Country/Territory | United Kingdom |
City | Glasgow |
Period | 17/03/2024 → 22/03/2024 |
Internet address |