Grating Lobe Suppression Based on FCN for Through-the-Wall 3D SAR Imaging

Zhongjie Ma*, Shichao Zhong, Xiaopeng Yang, Jiarong Zhao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In through-the-wall 3D synthetic aperture radar (SAR) imaging, under-sampling in the height direction is usually unavoidable, resulting in severe grating lobe effects. Traditional grating lobe suppression algorithms have poor performance, and existing neural network research is limited to 2D SAR images, which cannot achieve high-quality grating lobe suppression for 3D SAR. To solve this problem, this paper proposes a grating lobe suppression algorithm for through-the-wall 3D SAR based on fully convolutional networks (FCN). Simulation results demonstrate that, compared to traditional grating lobe suppression algorithms, the proposed network significantly suppresses grating lobe effects in 3D SAR, achieving optimal imaging quality. Finally, the effectiveness of the algorithm is demonstrated with measured data.

Original languageEnglish
Title of host publicationIEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331515669
DOIs
Publication statusPublished - 2024
Event2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024 - Zhuhai, China
Duration: 22 Nov 202424 Nov 2024

Publication series

NameIEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

Conference

Conference2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
Country/TerritoryChina
CityZhuhai
Period22/11/2424/11/24

Keywords

  • 3D synthetic aperture radar
  • Grating Lobe Suppression
  • neural network
  • through-the-wall radar(TWR)

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