Multichannel Signal Modeling and AMTI Performance Analysis for Distributed Space-Based Radar Systems

Jiangyuan Chen, Penghui Huang*, Xiang Gen Xia, Junli Chen, Yongyan Sun, Xingzhao Liu, Guisheng Liao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Due to the limited size, carrying capacity, power-aperture product, and high hardware cost of the satellite platform, the traditional single-platform spaceborne radar system encounters the problems of poor target minimum detectable velocity (MDV) performance, considerably deteriorating the moving target detection performance. To improve the air moving target indication (AMTI) performance, especially for a weak target, the distributed space-based radar system (DSBR) becomes a good candidate due to the longer along-track baseline (ATB) and spatial power synthesis. However, due to the sparse configuration of radar baseline distribution, the detection performance of air moving targets (AMTs) will be restricted by many practical factors in an actual DSBR system. In this article, multichannel signal models of an observed moving target and ground clutter are accurately established in a DSBR framework, where the error influences of cross-track baseline (CTB), terrain fluctuation, and channel inconsistency response are considered. Then, the influence of the nonideal factors, including the channel noise, long-intersatellite ATB, long-intersatellite CTB, synchronization errors, and interchannel amplitude and phase inconsistency errors, on the AMTI performance is analyzed term by term. The simulation results provide useful guidance for the system design of a DSBR with the AMTI tasks.

Original languageEnglish
Article number5117724
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume60
DOIs
Publication statusPublished - 2022
Externally publishedYes

Keywords

  • Air moving target indication (AMTI)
  • distributed space-based early warning radar
  • interchannel correlation analysis
  • spaceatime adaptive processing (STAP)

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