在对散焦星载合成孔径雷达(SAR)图像进行自聚焦处理时，卫星轨道的平滑特性对于成像处理非常有利。基于该特性，提出一种新的星载SAR自聚焦方法。首先将卫星轨道在三维空间分别用低阶多项式进行拟合，从而将自聚焦问题转化为对多项式的估计问题；然后，选取图像中分散的几个小图像切片，通过最大对比度法得到每个切片中心点的最优距离历程；再根据这几个距离历程调整整景轨道，最终得到能使各切片良好聚焦的轨道估计结果。通过合适的切片选取，可以使整幅图像良好聚焦。仿真实验和实际数据结果验证了该方法的有效性。
陈颖
,
孟大地
,
李光祚
,
黄丽佳
,
李莹莹
,
辛煜
. 基于对比度最优和低阶轨道多项式估计的星载SAR自聚焦方法[J]. 中国科学院大学学报, 2025
, 42(4)
: 508
-518
.
DOI: 10.7523/j.ucas.2023.035
Spaceborne synthetic aperture radar (SAR) imagery with higher spatial resolution requires greater knowledge of the satellite’s orbit. However, sometimes global positioning system (GPS) is not able to provide accurate position information that the image resolution requires. In our experience, the smoothness of spaceborne SAR orbit can be helpful for autofocus. In this paper, a novel approach for autofocusing in spaceborne SAR is proposed. First, the modeling of the orbit in three-dimensions as polynomial functions is involved. Therefore, autofocusing can be achieved by estimating the polynomial coefficients. Then, several patches distributed over the SAR image are selected, and the optimal range history of the center point in each patch is obtained based on the maximum-contrast optimization. The estimated orbit of the whole scene can be derived through the range history information. Finally, the image can be refined with better focusing performance. The estimated orbit is capable of satisfying the optimal quality for every patch. Furthermore, with proper patch selection, including the number and the relative location of patches, better quality within the whole scene can be reconstructed by the estimated orbit. This method is tested and validated with simulation experiments and real data.
[1] 李春升,于泽,陈杰. 高分辨率星载SAR成像与图像质量提升方法综述[J]. 雷达学报,2019,8(6):717-731. DOI:10.12000/JR19085.
[2] Meng D D, Ding C B, Hu D H, et al. On the processing of very high resolution spaceborne SAR data: a chirp-modulated back projection approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(1): 191-201. DOI: 10.1109/TGRS.2017.2744649.
[3] Fornaro G. Trajectory deviations in airborne SAR: analysis and compensation[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(3): 997-1009. DOI:10.1109/7.784069.
[4] Gao Y, Yu W D, Liu Y B, et al. Sharpness-based autofocusing for stripmap SAR using an adaptive-order polynomial model[J]. IEEE Geoscience and Remote Sensing Letters, 2014,11(6): 1086-1090. DOI:10.1109/LGRS.2013.2286410.
[5] Yu Z J, You Z. Real-time onboard orbit determination using GPS navigation solutions[C]//2011 First International Conference on Instrumentation, Measurement, Computer, Communication and Control. October 21-23, 2011, Beijing, China. IEEE, 2012: 949-952. DOI: 10.1109/IMCCC.2011.239.
[6] Muff D G, Blake A P, Horne A M. Spaceborne SAR autofocus[C]//IEE Colloquium on Recent Developments in Radar and Sonar Imaging Systems: What Next?. December 12-12, 1995, London, UK. London: IET, 2002: 11/1-1111. DOI: 10.1049/ic:19951576.
[7] 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.
[8] Chan H L, Yeo T S. Noniterative quality phase-gradient autofocus (QPGA) algorithm for spotlight SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(5): 1531-1539. DOI: 10.1109/36.718857.
[9] 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.
[10] Evers A, Jackson J A. A generalized phase gradient autofocus algorithm[J]. IEEE Transactions on Computational Imaging, 2019, 5(4): 606-619. DOI: 10.1109/TCI.2019.2899453.
[11] Xi L, Guosui L, Ni J L. Autofocusing of ISAR images based on entropy minimization[J]. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(4): 1240-1252. DOI:10.1109/7.805442.
[12] Wang J, Liu X. SAR minimum-entropy autofocus using an adaptive-order polynomial model[J]. IEEE Geoscience and Remote Sensing Letters, 2006, 3(4): 512-516. DOI:10.1109/LGRS.2006.878446.
[13] Yang L, Xing M, Zhang L, et al. Entropy-based motion error correction for high-resolution spotlight SAR imagery[J]. IET Radar, Sonar & Navigation, 2012, 6(7): 627-637. DOI:10.1049/iet-rsn.2011.0078.
[14] Fienup J R. Synthetic-aperture radar autofocus by maximizing sharpness[J]. Optics Letters, 2000, 25(4): 221-223. DOI: 10.1364/OL.25.000221.
[15] Fienup J R, Miller J J. Aberration correction by maximizing generalized sharpness metrics[J]. Journal of the Optical Society of America. A, Optics, Image Science, and Vision, 2003, 20(4): 609-620. DOI: 10.1364/josaa.20.000609.
[16] Berizzi F, Dalle Mese E, Martorella M. Performance analysis of a contrast-based ISAR autofocusing algorithm[C]//Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322). April 25-25, 2002, Long Beach, CA, USA. IEEE, 2002: 200-205. DOI: 10.1109/NRC.2002.999719.
[17] Berizzi F, Martorella M, Cacciamano A, et al. A contrast-based algorithm for synthetic range-profile motion compensation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(10): 3053-3062. DOI:10.1109/TGRS.2008.2002576.
[18] Berizzi F, Corsini G. Autofocusing of inverse synthetic aperture radar images using contrast optimization[J]. IEEE Transactions on Aerospace and Electronic Systems, 1996, 32(3): 1185-1191. DOI:10.1109/7.532282.
[19] Ran L, Liu Z, Zhang L, et al. An autofocus algorithm for estimating residual trajectory deviations in synthetic aperture radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(6): 3408-3425. DOI:10.1109/TGRS.2017.2670785.
[20] Liang Y, Li G F, Wen J, et al. A fast time-domain SAR imaging and corresponding autofocus method based on hybrid coordinate system[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 8627-8640. DOI:10.1109/TGRS.2019.2921917.
[21] Meng D D, Huang L J, Qiu X L, et al. A novel approach to processing very-high-resolution spaceborne SAR data with severe spatial dependence[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 7472-7482. DOI:10.1109/JSTARS.2022.3202932.
