A Prior 2-D Autofocus Algorithm With Ground Cartesian BP Imaging for Curved Trajectory SAR

Due to the complexity of the trajectory, the curved trajectory SAR signal is seriously two-dimensional (2-D) space-variant. The ground Cartesian back-projection (GCBP) algorithm is a fast time-domain algorithm without interpolation, which can meet the imaging accuracy of curved trajectory SAR. Howev...

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Veröffentlicht in:IEEE journal of selected topics in applied earth observations and remote sensing 2024, Vol.17, p.2422-2436
Hauptverfasser: Lou, Yishan, Lin, Hao, Li, Ning, Xing, Mengdao, Wang, Junrui, Wu, Zhixin
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Sprache:eng
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Zusammenfassung:Due to the complexity of the trajectory, the curved trajectory SAR signal is seriously two-dimensional (2-D) space-variant. The ground Cartesian back-projection (GCBP) algorithm is a fast time-domain algorithm without interpolation, which can meet the imaging accuracy of curved trajectory SAR. However, when the nonsystematic range cell migration of GCBP image caused by motion error exists, the existing autofocus algorithms find it difficult to correctly estimate the phase error. Therefore, an extended 2-D autofocus algorithm is proposed to realize curved trajectory SAR imaging by combining the GCBP algorithm, in this article. The expression of the spectrum support region of the GCBP image is given, and the spectrum characteristics are analyzed. Then, the spectrum alignment is performed based on these characteristics to eliminate spectrum aliasing. Finally, the analytical property of phase error in the 2-D frequency domain of the GCBP image is analyzed in detail. Based on the property, the prior 2-D phase error structure for the GCBP image is established. According to the structure, the 2-D phase error is computed and compensated to obtain the refocused image. In addition, the index grid division method embedded in the proposed algorithm can effectively compensate the space-variant phase error. The simulation and real data results prove the effectiveness of the proposed algorithm.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2023.3346942