Abstract
While computational microwave imaging (CMI) addresses several limitations of conventional microwave imaging techniques, such as hardware complexity, it is still constrained by substantial computational resources required for image reconstruction. This paper presents a convolutional neural network (CNN)-based approach to enhance the computational efficiency of CMI. The proposed network directly computes the transfer function, or sensing matrix, from the aperture fields of antennas within a CMI system. To improve information extraction, convolutional block attention modules (CBAMs) are integrated into the architecture. Numerical results on a testing dataset demonstrate an average normalized mean squared error (NMSE) of 0.054. Compared to conventional methods, the proposed network reduces computation time by 69% in generating the sensing matrix. Overall, the network generates the sensing matrix from two different sets of aperture field distributions with high precision, achieving considerable computational savings for CMI applications.
Original language | English |
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Title of host publication | Proceedings of the 19th European Conference on Antennas and Propagation, EuCAP 2025 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Number of pages | 5 |
Publication status | Accepted - 31 Dec 2024 |
Event | 19th European Conference on Antennas and Propagation 2025 - Stockholm, Sweden Duration: 30 Mar 2025 → 04 Apr 2025 https://eucap.org/ |
Publication series
Name | EuCAP Proceedings |
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ISSN (Print) | 2164-3342 |
Conference
Conference | 19th European Conference on Antennas and Propagation 2025 |
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Abbreviated title | EuCAP 2025 |
Country/Territory | Sweden |
City | Stockholm |
Period | 30/03/2025 → 04/04/2025 |
Internet address |
Keywords
- microwave imaging
- computational microwave imaging
- deep learning
- sensing matrix