A Ship Target Calibration Method Based on GRFT

  • Jiaqi Lei*
  • , Han Li
  • , Shujiang Liu
  • , Pengnan Zheng
  • , Zhang Yan
  • , Chengxin Zheng
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Calibration is a key technology in ISAR (Inverse Synthetic Aperture Radar) processing. Traditional calibration techniques are typically aimed at uniformly oscillating targets. However, for sea surface moving targets, their oscillation exhibits significant sinusoidal characteristics, reducing the performance of traditional algorithms. To address this issue, this paper proposes a GRFT-based (Generalized Radon-Fourier Transform) calibration method for sea surface ships. Based on the construction of the ship's echo model, the relationship between the motion parameters and the calibration-required angular parameters is established. The PSO-GRFT-CLEAN (Particle Swarm Optimization, PSO) algorithm is then used to estimate the motion parameters of the two sub-apertures, completing the calibration of the ISAR image. Simulations on ship models demonstrate the effectiveness of the algorithm.

Original languageEnglish
Title of host publicationIEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331515669
DOIs
Publication statusPublished - 2024
Event2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024 - Zhuhai, China
Duration: 22 Nov 202424 Nov 2024

Publication series

NameIEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

Conference

Conference2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
Country/TerritoryChina
CityZhuhai
Period22/11/2424/11/24

Keywords

  • Calibration
  • GRFT
  • ISAR
  • Ship Target

Fingerprint

Dive into the research topics of 'A Ship Target Calibration Method Based on GRFT'. Together they form a unique fingerprint.

Cite this