Near range radar imaging by SFCW linear sparse array based on block sparsity

Weike Feng, Li Yi, Motoyuki Sato

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

3 Citations (Scopus)

Abstract

A novel compressive sensing (CS) based imaging method for SFCW radar near range targets is proposed. Different from existing CS based methods, the azimuth-dependency of the target reflection coefficient is considered. Based on the block sparsity property of the received signal in the proposed sparsifying dictionary, the 2D image of targets can be obtained at each spatial sampling point. Cross-correlation method is then employed to fuse these 2D images to get the final result. Compared to the classical back projection (BP) method, the proposed method can obtain higher resolution with fewer artifacts via fewer frequencies. Compared to the conventional CS based method, artifacts can be significantly reduced. Experiment results of a SFCW linear sparse array radar system demonstrate that, with only 1/8 data, the proposed method can achieve accurate high-resolution 2D image of targets in the near range. In 30dB dynamic range, no artifact was produced on the imaging result.

Original languageEnglish
Title of host publication2017 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publicationInternational Cooperation for Global Awareness, IGARSS 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5362-5365
Number of pages4
ISBN (Electronic)9781509049516
DOIs
Publication statusPublished - 2017 Dec 1
Event37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, United States
Duration: 2017 Jul 232017 Jul 28

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2017-July

Other

Other37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
CountryUnited States
CityFort Worth
Period17/7/2317/7/28

Keywords

  • Block sparsity
  • Compressive sensing (CS)
  • Cross-correlation
  • Gb-SAR
  • Stepped-frequency continuous waveform (SFCW)

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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