ISAR translational motion compensation method based on azimuth partitioning

doi:10.7523/j.ucas.2026.008

Abstract

Abstract: Inverse synthetic aperture radar (ISAR) imaging is an important technique for observing non-cooperative targets such as aircraft and satellites. Traditional translational motion compensation methods typically consist of envelope alignment and phase adjustment. However, under long-range and long-duration observation conditions, the echo amplitude and signal-to-noise ratio usually vary over time, leading to degraded compensation performance. To address this issue, this paper proposes an effective translational motion compensation method that adaptively segments the azimuth dimension based on the two-dimensional distribution of echo amplitude. Different segmentation strategies are employed for envelope alignment and phase adjustment, and motion compensation is performed within each sub-aperture. Subsequently, azimuthal envelope alignment and minimum-entropy optimization are used to correct linear and constant phase offsets between sub-apertures, thereby completing sub-block stitching and achieving full-aperture motion compensation. Experimental results on real data verify the effectiveness of the proposed method.

Key words: adaptive partitioning, inverse synthetic aperture radar, translational motion compensation, envelope alignment, phase gradient autofocus

CLC Number:

TN957

LIU Zilong, LI Shiqiang. ISAR translational motion compensation method based on azimuth partitioning[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2026.008.

References

[1] Li N, Wang W B, Shu G F, et al.Influence factors and performance degradation of space-to-space ISAR imaging induced by orbital mutual inclination and altitude[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 5206418. DOI:10.1109/TGRS.2025.3548436.
[2] Yang S H, Li S Q, Fan H T, et al.High-resolution ISAR imaging of maneuvering targets based on azimuth adaptive partitioning and compensation function estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5222115. DOI:10.1109/TGRS.2023.3334770.
[3] 田彪, 刘洋, 呼鹏江, 等. 宽带逆合成孔径雷达高分辨成像技术综述[J]. 雷达学报, 2020, 9(5): 765-802. DOI:10.12000/JR20060.
[4] Yang S H, Li S Q, Jia X X, et al.An efficient translational motion compensation approach for ISAR imaging of rapidly spinning targets[J]. Remote Sensing, 2022, 14(9): 2208. DOI:10.3390/rs14092208.
[5] Li D, Zhan M Y, Liu H Q, et al.A robust translational motion compensation method for ISAR imaging based on keystone transform and fractional Fourier transform under low SNR environment[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(5): 2140-2156. DOI:10.1109/TAES.2017.2683599.
[6] 莫俊贤, 张月, 陈锦帆, 等. 基于PSO联合运动补偿的ISAR成像算法[J]. 现代雷达, 2024, 46(7): 52-61. DOI:10.16592/j.cnki.1004-7859.2024.07.007.
[7] 苏白华宁, 刘畅, 王超. 基于参数估计与子孔径提取的船舶ISAR实时成像算法[J]. 中国科学院大学学报, 2023, 40(5): 647-657. DOI: 10.7523/j.ucas.2022.016.
[8] 侯庆森, 李光祚, 徐仲秋, 等. 基于联合运动参数快速估计的空间目标ISAR成像方法[J]. 雷达学报, 2025, 14(2): 424-438. DOI:10.12000/JR24251.
[9] Wang J F, Kasilingam D.Global range alignment for ISAR[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(1): 351-357. DOI:10.1109/TAES.2003.1188917.
[10] 田坤, 刘霖, 曹越, 等. 基于FPGA的船只目标ISAR实时重聚焦处理实现[J]. 电子设计工程, 2022, 30(8): 16-20, 25. DOI:10.14022/j.issn1674-6236.2022.08.004.
[11] Zhu D Y, Wang L, Yu Y S, et al.Robust ISAR range alignment via minimizing the entropy of the average range profile[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(2): 204-208. DOI: 10.1109/LGRS.2008.2010562.
[12] Wahl D E, Eichel P H, Ghiglia D C, et al.Phase gradient autofocus-a robust tool for high resolution SAR phase correction[J]. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(3): 827-835. DOI:10.1109/7.303752.
[13] Özdemir C.Inverse Synthetic Aperture Radar Imaging with MATLAB® Algorithms: With Advanced SAR/ISAR Imaging Concepts, Algorithms, and MATLAB® Codes[M]. 2nd ed. Hoboken: Wiley, 2021. DOI:10.1002/9781119521396.
[14] Shi S, Zhang H, Deng Y K, et al.Azimuth envelope alignment for focusing maneuvering ships in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 5207815. DOI:10.1109/TGRS.2025.3549192.
[15] Jiang Y C, Ni H, Chen R D, et al.Translational motion compensation method for ISAR imaging of air maneuvering weak targets based on CV-GRUNet[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 4003205. DOI:10.1109/LGRS.2024.3359285.
[16] 王勇, 夏浩然, 刘明帆. 基于复数域卷积神经网络的ISAR包络对齐方法研究[J]. 信号处理, 2025, 41(3): 409-425.
[17] 刘一飞,禹卫东,杨升辉,等. 天基ISAR对空间轨道目标高分辨率成像问题分析[J].系统工程与电子技术, 2024, 46(11): 3671-3683. DOI:10.12305/j.issn.1001-506X.2024.11.09.
[18] Liu Y F, Yu W D, Yang S H, et al.An effective space-borne ISAR high-resolution imaging approach for satellite on-orbit based on minimum entropy optimization[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 4523-4537. DOI:10.1109/JSTARS.2024.3359264.
[19] Yang S H, Li S Q, Fan H T, et al.An effective translational motion compensation approach for high-resolution ISAR imaging with time-varying amplitude[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 4007805. DOI:10.1109/LGRS.2023.3296702.
[20] Ye W, Yeo T S, Bao Z.Weighted least-squares estimation of phase errors for SAR/ISAR autofocus[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2487-2494. DOI:10.1109/36.789644.
[21] 徐刚, 杨磊, 张磊,等.一种加权最小熵的ISAR自聚焦算法[J].电子与信息学报, 2011, 33(8): 1809-1815. DOI:10.3724/SP.J.1146.2010.01153.
[22] Li J C, Chen J, Wang P B, et al.A coarse-to-fine autofocus approach for very high-resolution airborne stripmap SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(7): 3814-3829. DOI:10.1109/TGRS.2018.2812240.
[23] Zhu D Y, Jiang R, Mao X H, et al.Multi-subaperture PGA for SAR autofocusing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(1): 468-488. DOI:10.1109/TAES.2013.6404115.
[24] Zhang S H, Liu Y X, Li X.Fast entropy minimization based autofocusing technique for ISAR imaging[J]. IEEE Transactions on Signal Processing, 2015, 63(13): 3425-3434. DOI:10.1109/TSP.2015.2422686.
[25] Kang M S.Robust ISAR autofocus via Newton-based Tsallis entropy minimization[J]. IEEE Geoscience and Remote Sensing Letters, 2025, 22: 3505905. DOI:10.1109/LGRS.2025.3596922.
[26] Meng Z C, Zhang L, Ma Y, et al.Accelerating minimum entropy autofocus with stochastic gradient for UAV SAR imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4017805. DOI: 10.1109/LGRS.2021.3106636.
[27] 邢孟道. 基于实测数据的雷达成像方法研究[D]. 西安: 西安电子科技大学, 2002.
[28] Huang P H, Xia X G, Zhan M Y, et al.ISAR imaging of a maneuvering target based on parameter estimation of multicomponent cubic phase signals[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5103918. DOI:10.1109/TGRS.2021.3091645.
[29] Wang J D, Zhang L, Du L, et al.Noise-robust motion compensation for aerial maneuvering target ISAR imaging by parametric minimum entropy optimization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(7): 4202-4217. DOI:10.1109/TGRS.2018.2890098.