Learning assisted sensing matrix calculation in computational microwave imaging

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings of the 19th European Conference on Antennas and Propagation, EuCAP 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
Publication statusAccepted - 31 Dec 2024
Event19th European Conference on Antennas and Propagation 2025 - Stockholm, Sweden
Duration: 30 Mar 202504 Apr 2025
https://eucap.org/

Publication series

NameEuCAP Proceedings
ISSN (Print)2164-3342

Conference

Conference19th European Conference on Antennas and Propagation 2025
Abbreviated titleEuCAP 2025
Country/TerritorySweden
CityStockholm
Period30/03/202504/04/2025
Internet address

Keywords

  • microwave imaging
  • computational microwave imaging
  • deep learning
  • sensing matrix

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