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Ship Motion Parameter Estimation and Refocusing Method Based on Minimization of Position Offset in Multiview Missile-Borne SAR Images
Author(s) -
Yihang Zhi,
Bing Sun,
Jingwen Li,
Mingqing Han,
Ziyue Yang
Publication year - 2025
Publication title -
ieee journal of selected topics in applied earth observations and remote sensing
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 1.246
H-Index - 88
eISSN - 2151-1535
pISSN - 1939-1404
DOI - 10.1109/jstars.2025.3590030
Subject(s) - geoscience , signal processing and analysis , power, energy and industry applications
Motion compensation helps mitigate the defocusing effects observed in ship synthetic aperture radar (SAR) images caused by ship's noncooperative motion, and its effectiveness depends on the accuracy of motion parameter estimation. Although distributed radar systems may incorporate corresponding points(CPs) in multiview to increase the accuracy of motion parameter estimation, the complex scattering characteristics of ships make it challenging to identify CPs, thereby degrading motion parameter estimation accuracy. To overcome this limitation, this paper introduces a motion parameter estimation and refocusing method based on minimizing position offset in multiview images. The proposed method formulates a minimization model on the basis of multiview imaging position offset and accomplishes refocusing while estimating the two dimensional velocity and position of the ship. To mitigate the impact of scattering characteristic variations on parameter estimation accuracy, a spectral residual method enhanced by minimum bounding rectangle is introduced to reduce ship's imaged position calculation errors across viewing angles. Compared with image quality optimization-based estimation methods, the proposed position offset minimization method achieves a 2.2 m/s improvement in velocity estimation accuracy and a 121.8 meter enhancement in positioning. The cross-correlation similarity between the refocused images and static ship images reaches 0.98, representing a 9% improvement over that of the phase gradient autofocus method.

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