A Novel Multi-Scan Joint Method for Slow-Moving Target Detection in the Strong Clutter via RPCA

Jia Su, Guonan Cui, Tao Li, Yifei Fan, Mingliang Tao, Haitao Wang, Xiang Zhang

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

Slow-moving target detection in strong clutter background is a critical issue for the ground-based radar system. To detect the slow-moving target effectively, a novel multi-scan joint target detection method via principal component analysis (RPCA) is proposed. For radar echoes, there are two useful properties: 1) Stationary ground clutters have low-rank property, since the clutters in adjacent scan intervals are almost similar; 2) Moving targets have the sparse characteristic, due to their variation of position and sparsely distributed. Thanks to these two properties, moving targets can be separated from the stationary clutters via RPCA. Compared with the moving target indicator (MTI) method, the experimental results demonstrate that the proposed algorithm not only can suppress clutters effectively, but also preserve the moving target as much as possible.

Original languageEnglish
Pages4787-4789
Number of pages3
DOIs
StatePublished - 2021
Event2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium
Duration: 12 Jul 202116 Jul 2021

Conference

Conference2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/TerritoryBelgium
CityBrussels
Period12/07/2116/07/21

Keywords

  • Slow-moving target detection
  • clutter suppression
  • robust principal component analysis(RPCA)

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