Moving target imaging using GNSS-based passive bistatic synthetic aperture radar

Zhen Yu He, Yang Yang, Wu Chen, Duo Jie Weng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


Current studies of global navigation satellite systems (GNSS)-based bistatic synthetic aperture radar (GNSS-SAR) is focused on static objects on land. However, moving target imaging is also very significant for modern SAR systems. Imaging a moving target has two main problems. One is the unknown range cell migration; the other is the motion parameter estimation, such as the target’s velocity. This paper proposes a moving target imaging formation algorithm for GNSS-SAR. First, an approximate bistatic range history is derived to describe the phase variation of the target signal along the azimuth time. Then, a keystone transform is employed to correct the range cell migration. To address the motion parameter estimation, a chirp rate estimation method based on short-time Fourier transform and random sample consensus is proposed with high processing efficiency and robust estimation errors in low signal-to-noise ratio scenes. The estimated chirp rate can calculate the target’s velocity. Finally, azimuth compression derivation is performed to accomplish GNSS-SAR imaging. A maritime experimental campaign is conducted to validate the effectiveness of the proposed algorithm. The two cargo ships in the SAR images have good accordance with the ground truth in terms of the target-to-receiver vertical distances along the range and the ships’ length along the cross-range.

Original languageEnglish
Article number3356
Pages (from-to)1-21
Number of pages21
JournalRemote Sensing
Issue number20
Publication statusPublished - 2 Oct 2020


  • Image formation algorithm
  • Keystone transform
  • Random sample consensus
  • Short-time Fourier transform

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)


Dive into the research topics of 'Moving target imaging using GNSS-based passive bistatic synthetic aperture radar'. Together they form a unique fingerprint.

Cite this