Multichannel Sea Clutter Modeling for Spaceborne Early Warning Radar and Clutter Suppression Performance Analysis

Penghui Huang, Zihao Zou*, Xiang Gen Xia, Xingzhao Liu, Guisheng Liao, Zhihui Xin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)

Abstract

In this article, we propose a multichannel sea clutter model in a spaceborne early warning radar system and analyze the influence of the sea clutter motion characteristics on the space-time adaptive processing (STAP) performance. To establish a multichannel sea clutter model, the 3-D Gerstner wave model is applied to construct the sea surface. Then the Pierson-Moskowitz wave spectrum and the stereo wave observation project (SWOP) directional spectrum are combined to describe the amplitude distribution of waves in different frequencies and directions. At the same time, the two-scale model is applied to obtain the specific backscattering coefficients of sea clutter at different time and positions. In addition, breaking waves are added in sea clutter returns with the form of false targets. Finally, the space-time distribution characteristics of sea clutter in a spaceborne multichannel array system and the influences of sea clutter under different wind speeds and directions on STAP performance are analyzed based on the simulation processing results. Processing results of some real-measured radar data are also exhibited to verify the theoretical analyses.

Original languageEnglish
Pages (from-to)8349-8366
Number of pages18
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume59
Issue number10
DOIs
Publication statusPublished - Oct 2021
Externally publishedYes

Keywords

  • Clutter motion
  • clutter suppression
  • multichannel sea clutter modeling
  • space-time adaptive processing (STAP)
  • spaceborne early warning radar

Fingerprint

Dive into the research topics of 'Multichannel Sea Clutter Modeling for Spaceborne Early Warning Radar and Clutter Suppression Performance Analysis'. Together they form a unique fingerprint.

Cite this