An Autofocus Back Projection Algorithm for GEO SAR Based on Minimum Entropy

Ding, Zegang; Zhu, Kaiwen; Zhang, Tianyi; Li, Linghao; Wang, Yan; Wang, Guanxing; Gao, Yongpeng; Wei, Yangkai; Zeng, Tao

doi:10.1109/tgrs.2022.3164922

Cited by 11 publications

(4 citation statements)

References 44 publications

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“…Leping Chen proposes an extended autofocus BP algorithm based on the maximum image sharpness criterion by selecting regions and balancing data energy [47]. Zegang Ding proposes an autofocus algorithm based on minimum entropy and adaptive moment estimation combined with the FFBP algorithm [48]. However, these algorithms need to search for the optimal motion parameters iteratively, which have high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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

Lou,

Lin,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Due to the complexity of the trajectory, the curved trajectory SAR signal is seriously two-dimensional (2-D) spacevariant. 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 (NsRCM) of GCBP image caused by motion error exists, the existing autofocus algorithms is 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 paper. 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. Index Terms-Ground Cartesian back-projection (GCBP), synthetic aperture radar (SAR), curved trajectory, spectrum alignment, 2-D phase error, prior structure, index grid division algorithm.

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Section: Introductionmentioning

confidence: 99%

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

Lou,

Lin,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Therefore, the focus of SAR research has gradually shifted from ideal configurations to complex configurations. Various improved SAR imaging algorithms have emerged [9][10][11][12]. Azimuth Missing Data (AMD) SAR imaging problem, an unavoidable imaging difficulty in practice, has attracted attention for nearly 30 years [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Azimuth Missing Data SAR Imaging Based on Sparse Representation Autofocusing

Jiang

et al. 2023

Remote Sensing

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Due to significant electromagnetic interference, radar interruptions, and other factors, Azimuth Missing Data (AMD) may occur in Synthetic Aperture Radar (SAR) echo, resulting in severe defocusing and even false targets. An important approach to solving this problem is to utilize Compressed Sensing (CS) methods on AMD echo to reconstruct complete echo, which can be abbreviated as the AMD Imaging Algorithm (AMDIA). However, the State-of-the-Art AMDIA (SOA-AMDIA) do not consider the influence of motion phase errors, resulting in an unacceptable estimation error of the complete echo reconstruction. Therefore, in order to enhance the practical applicability of AMDIA, this article proposes an improved AMDIA using Sparse Representation Autofocusing (SRA-AMDIA). The proposed SRA-AMDIA aims to accurately focus the imaging result, even in the Phase Error AMD (PE-AMD) echo case. Firstly, a Phase-Compensation Function (PCF) based on the phase history of the scene centroid is designed. When the PCF is multiplied with the PE-AMD echo in the range-frequency domain, a coarse-focused sparse representation signal can be obtained in the range-Doppler domain. However, due to the influence of unknown PE, the sparsity of this sparse representation signal is unsatisfying, breaking the sparse constraints requirement of the CS method. Therefore, we introduced a minimum entropy autofocusing algorithm to autofocus this sparse representation signal. Next, the estimated PE is compensated for this sparse representation signal, and a more sparse representation signal is obtained. Hence, the non-PE complete echo can be reconstructed. Finally, the estimated complete echo can be used with classic imaging algorithms to obtain high-resolution imaging results under the PE-AMD condition. Simulation and real measured data have verified the effectiveness of the proposed SRA-AMDIA.

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“…Furthermore, Li et al use contrast optimization autofocus (COA) to compensate for the time-variant ionospheric effect on GEO-SAR focusing, but the requirement of a strong target in the image is still indispensable [24]. Hu and Ding have applied autofocus processing based on minimum entropy to compensate for the ionospheric effects on GEO-SAR imaging, but Hu mainly concentrates on scintillation compensation [25], and Ding's method considers the different types of imaging errors as a whole including the ionosphere [26]. In addition, Zhang et al rely on the optimization processing of coherent character to solve background ionospheric phase distortion [27].…”

Section: Introductionmentioning

confidence: 99%

Compensation of Background Ionospheric Effect on L-Band Geosynchronous SAR with Fully Polarimetric Data

2023

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The L-band geosynchronous synthetic aperture radar (GEO-SAR) has been widely praised for its advantages of short revisit time, wide coverage and stable backscattering information acquisition. However, due to the ultra-long integrated time, the echo will be affected by the time-variant background ionosphere, leading in particular to defocusing in the azimuth direction. Existing compensation methods suitable for low Earth orbit SAR (LEO-SAR) are based on the SAR image or the semi-focused image at the ionospheric phase screen, assuming that the ionosphere is time-frozen for a short integrated period; thus, accurate reconstruction of the time-variant characteristics for the ionosphere in GEO-SAR cannot be achieved. In this paper, a compensation method of background ionospheric effects on L-band GEO-SAR with fully polarimetric data is proposed. Considering the continuous variation of the ionosphere within the synthetic aperture, a decompression processing is proposed to reconstruct the echo by recovering the temporal sampling according to the imaging geometry. By virtue of the Faraday rotation angle, the time-variant total electron content (TEC) is accurately estimated with the reconstructed echo. Based on the established error model, the ionospheric effects are well compensated with the estimated TEC. Simulations with the real SAR data from ALOS-2 and the measured time-variant TEC from USTEC validate the effectiveness and performance of the proposed method. The impacts from thermal noise and polarimetric calibration error are also quantitatively analyzed. From this, the error thresholds are given to guarantee compensation accuracy, namely 18.96 dB for SNR, −15.63 dB for crosstalk and −1.02 dB to 0.31 dB for the amplitude of the channel imbalance, and the argument of the channel imbalance is suggested to be maintained as close to zero as possible.

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An Autofocus Back Projection Algorithm for GEO SAR Based on Minimum Entropy

Cited by 11 publications

References 44 publications

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

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

High-Resolution Azimuth Missing Data SAR Imaging Based on Sparse Representation Autofocusing

Compensation of Background Ionospheric Effect on L-Band Geosynchronous SAR with Fully Polarimetric Data

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