A Distributed Satellite Formation Method for SAR Three-Dimensional Tomography Applications
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摘要: SAR三维层析成像应用中要求对同一地点多次观测积累数据才能实现高分辨率、高精度成像，而星载系统需要多次的重轨观测实现这一任务，这就需要频繁的轨控以形成所需要的卫星高度进行观测，这需要巨大的系统代价才能实现，即便如此时间去相关的影响也无法消除。分布式编队卫星通过多颗卫星编队飞行的方式，可以无时间去相关的实现对同一地区的同时多站观测，通过合理设计编队构型，实现空中观测阵列，完成单次航过下的高精度高分辨率三维SAR成像。本文根据编队卫星相对运动模型，提出并建立了RTN坐标系下分布式卫星三维基线与轨道六根数的直接数学关系模型，以此作为理论依据设计了并排多星的嵌套车轮编队构型和横向并排编队构型，满足“一字形”编队观测阵列需求，并进一步分析了空间摄动下的编队构型发散变化分析，提出相应的构型维持策略，并对该构型进行了SAR三维成像仿真分析。仿真结果表明，本文所设计的编队构型经过合理的轨道参数设计，可以提供三维层析成像所需的垂直孔径基线，构型维持策略简单易操作，且该构型提供的稳定层析向基线可获得更好的SAR三维层析成像性能，更容易满足编队任务的执行，具有较好的工程实现价值。
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关键词:
- 3D SAR层析成像 /
- 分布式卫星编队 /
- 并排多星编队构型 /
- 垂直孔径基线
Abstract:Objective SAR three-dimensional tomography technology can not only overcome the issues of overlapping, perspective foreshortening, and shadowing encountered in two-dimensional SAR imaging, but also resolve the problem of height-upward scattering-point resolution, achieving true three-dimensional high-precision imaging of targets. In SAR three-dimensional tomography applications, high-resolution and high-precision imaging requires multiple observations and data accumulation over the same location. Satellite systems must conduct repeated overpass observations to accomplish this task, necessitating frequent orbit control to maintain the required satellite altitude for observation, which incurs significant system costs. Even so, the impact of temporal decorrelation cannot be eliminated. Distributed formation satellites can achieve simultaneous multi-station observation of the same region through coordinated flight of multiple satellites. However, the current typical distributed formation systems are not optimal for SAR three-dimensional tomography. This paper proposes an array formation design method tailored for tomography applications, enabling the creation of a distributed satellite airborne observation array through rational formation design. This approach achieves high-precision, high-resolution three-dimensional SAR imaging in a single pass, while also proposing formation maintenance strategies based on the stability of actual in-orbit formations, demonstrating practical application value. Methods To meet the elevation-direction array imaging requirements for SAR 3D tomography, a distributed SAR satellite three-dimensional baseline mathematical model is established by modeling the relative motion of formation satellites, which is then converted into RTN coordinates to construct the elevation-direction synthetic aperture, normal baseline, and along-track baseline required for SAR tomography. This establishes a direct correspondence between the three-dimensional baseline and the six orbital elements of formation satellites, enabling the desired baseline distribution to be achieved by directly designing the orbital parameters of formation satellites. Based on the linear array observation requirements, two types of multi-satellite formations are designed: a nested wheel abreast formation with vertically aligned satellites and a lateral abreast formation with satellites aligned along the track. Each satellite in the formation orbits the configuration center in circular and harmonic relative motion, forming an array formation at any given moment. The spatial baselines formed by the multi-satellite formations designed using this method all satisfy the array observation requirements for SAR 3D tomography applications. The article also includes simulation analysis of formation configuration divergence and proposes corresponding formation maintenance strategies, demonstrating strong engineering feasibility. Results and Discussions A nested wheel side-by-side multi-satellite formation is designed to construct a vertical aperture baseline, with the normal baseline set to zero and the latitude angles ua=uc. The eccentricity, semi-major axis, and perigee angle of the main satellite and follower satellites are configured according to the required vertical aperture baseline length. The latitude angle design is determined based on the required observation latitude, ensuring the maximum radial baseline occurs at the desired observation position, resulting in a radial baseline maximum amplitude of $ a{e}_{a} $, yielding a vertically aligned linear distributed SAR formation model ( Figure 3 Configuration diagram of nested wheel side-by-side multi-satellite formation,Figure 4 Baseline distribution diagram of nested wheel side-by-side multi-satellite formation). To construct a normal baseline, a transverse side-by-side multi-satellite formation is designed with the radial baseline set to zero. The follower satellite's eccentricity ea is set to 0, and the right ascension difference of the ascending node ΔΩ between the main satellite and follower satellites is configured to achieve the normal baseline, with a maximum amplitude of $ |{a}_{c}({\Omega }_{a}-{\Omega }_{c})\sin {i}_{c}| $. This results in a transversely aligned linear four-satellite formation (Figure 5 Configuration diagram of transverse side-by-side multi-satellite formation,Figure 6 Baseline distribution diagram of transverse side-by-side multi-satellite formation). Both configurations provide stable array observation baselines to meet SAR 3D tomographic imaging requirements. Taking the nested wheel side-by-side four-satellite formation as an example, this study analyzes configuration divergence under atmospheric drag effects. Based on semi-major axis attenuation-induced along-track drift, a configuration maintenance strategy is proposed where the maximum elliptical center drift difference of the relative motion between two satellite groups should not exceed Lmax. When$ \left| {\Delta }_{1}-{\Delta }_{2}\right| \leq {L}_{\max } $, the formation can perform SAR imaging tasks normally; when$ \left| {\Delta }_{1}-{\Delta }_{2}\right| \geq {L}_{\max } $, configuration maintenance operations are required. Comparative simulation analysis of SAR 3D tomographic imaging between the nested wheel side-by-side four-satellite formation and the cartwheel formation with a central master satellite and orbiting auxiliary satellites. Within the incidence angle range, the coherence coefficient of the proposed configuration is consistently higher than that of the cartwheel configuration. This reduces coherence loss caused by along-track separation of formation satellites, thereby facilitating higher 3D imaging accuracy (Figure 12 Comparison of doppler spectrum decorrelation with ground incidence angle).Conclusions In summary, the distributed satellite formation with nested wheel side-by-side four-satellite formation designed according to the method in this article can provide stable vertical synthetic aperture length for 3D tomographic imaging. The formation configuration maintenance strategy is simple and easy to implement, and has good engineering feasibility.The distributed satellite formation system has the advantage of obtaining multiple observations in a single flight, overcoming the disadvantage of time decorrelation in traditional multi flight observations, and has unique advantages in achieving high-precision and high-resolution 3D tomographic SAR imaging. The nested wheel formation configuration and the lateral parallel formation configuration designed in this article can form a stable vertical aperture baseline in the vertical direction for systematic linear observation. Simulation results show that the divergence drift of the nested wheel side-by-side multi-satellite formation configuration in coplanar flight in space is mainly caused by the attenuation of the semi major axis. The stability of the formation configuration can be maintained by controlling the attenuation speed of the semi major axis to meet the execution of the formation task. The calculation results align with actual formation simulation outcomes.Imaging simulation results indicate that the proposed distributed satellite formation, featuring a nested cartwheel configuration with four side-by-side satellites, can provide stable tomographic baselines for SAR 3D tomographic imaging, reduce coherence loss caused by along-track separation of the formation satellites, and enhance 3D imaging performance.The research results of this article can provide technical references for the design of next-generation 3D tomographic SAR imaging systems. -
表 1 编队四星轨道参数
卫星 半长轴/km 偏心率 轨道倾角/° 升交点赤经差/° 近地点幅角/° 真近点角/° 卫星A 6873.68 0.00007274 97.39 0 180 180 卫星B 6873.68 0.00002425 97.39 0 180 180 卫星C 6873.68 0.00002425 97.39 0 0 0 卫星D 6873.68 0.00007274 97.39 0 0 0 
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[1] 丁赤飚, 仇晓兰, 吴一戎. 全息合成孔径雷达的概念、体制和方法[J]. 雷达学报, 2020, 9(3): 399–408. doi: 10.12000/JR20063.DING Chibiao, QIU Xiaolan, and WU Yirong. Concept, system, and method of holographic synthetic aperture radar[J]. Journal of Radar, 2020, 9(3): 399–408. doi: 10.12000/JR20063. [2] DING Zegang, ZHAO Jian, WANG Zhen, et al. Multi-master TomoSAR 3-D imaging: Theoretical complement and performance extension[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5218718. doi: 10.1109/TGRS.2024.3433033. [3] 魏恋欢, 廖明生, BALZ T, 等. 高分辨率SAR层析成像建筑物叠掩散射体提取[J]. 武汉大学学报: 信息科学版, 2014, 39(5): 536–540. doi: 10.13203/j.whugis20120460.WEI Lianhuan, LIAO Mingsheng, BALZ T, et al. Layover building scatterers extraction via high-resolution spaceborne SAR tomography[J]. Geomatics and Information Science of Wuhan University, 2014, 39(5): 536–540. doi: 10.13203/j.whugis20120460. [4] 杨牡丹, 魏中浩, 徐志林, 等. 一种基于层次稀疏的全极化SAR层析成像方法[J]. 中国科学院大学学报, 2020, 37(4): 525–531. doi: 10.7523/j.issn.2095-6134.2020.04.012.YANG Mudan, WEI Zhonghao, XU Zhilin, et al. A full-polarimetric SAR tomography method based on hierarchical sparseness[J]. Journal of University of Chinese Academy of Sciences, 2020, 37(4): 525–531. doi: 10.7523/j.issn.2095-6134.2020.04.012. [5] 焦润之, 邓嘉, 韩亚权, 等. 3DSARBuSim 1.0: 人造建筑高分辨星载SAR三维成像仿真数据集[J]. 电子与信息学报, 2024, 46(7): 2681–2693. doi: 10.11999/JEIT230882.JIAO Runzhi, DENG Jia, HAN Yaquan, et al. 3DSARBuSim 1.0: High-resolution space borne SAR 3D imaging simulation dataset of man-made buildings[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2681–2693. doi: 10.11999/JEIT230882. [6] KRIEGER G, FIEDLER H, MITTERMAYER J, et al. Analysis of multistatic configurations for spaceborne SAR interferometry[J]. IEE Proceedings-Radar, Sonar and Navigation, 2003, 150(3): 87–96. doi: 10.1049/ip-rsn:20030441. [7] KRIEGER G, MOREIRA A, FIEDLER H, et al. TanDEM-X: A satellite formation for high-resolution SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(11): 3317–3341. doi: 10.1109/TGRS.2007.900693. [8] ZINK M, MOREIRA A, HAJNSEK I, et al. TanDEM-X: 10 years of formation flying bistatic SAR interferometry[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 3546–3565. doi: 10.1109/JSTARS.2021.3062286. [9] MOREIRA A, KRIEGER G, HAJNSEK I, et al. Tandem-L: A highly innovative bistatic SAR mission for global observation of dynamic processes on the Earth’s surface[J]. IEEE Geoscience and Remote Sensing Magazine, 2015, 3(2): 8–23. doi: 10.1109/MGRS.2015.2437353. [10] ZINK M and MOREIRA A. TanDEM-X mission status: The new topography of the Earth takes shape[C]. 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, Canada, 2014: 3386–3389. doi: 10.1109/IGARSS.2014.6947207. [11] 杨元喜, 王建荣, 楼良盛, 等. 航天测绘发展现状与展望[J]. 中国空间科学技术, 2022, 42(3): 1–9. doi: 10.16708/j.cnki.1000-758X.2022.0031.YANG Yuanxi, WANG Jianrong, LOU Liangsheng, et al. Development status and prospect of satellite-based surveying[J]. Chinese Space Science and Technology, 2022, 42(3): 1–9. doi: 10.16708/j.cnki.1000-758X.2022.0031. [12] 朱泽尧, 赵双明, 张文, 等. 宏图一号分布式车轮编队InSAR卫星高程反演方法[J]. 武汉大学学报: 信息科学版, 2025, 50(8): 1619–1630. doi: 10.13203/j.whugis20240501.ZHU Zeyao, ZHAO Shuangming, ZHANG Wen, et al. Elevation inversion method for HT-1 distributed cartwheel InSAR satellite system[J]. Geomatics and Information Science of Wuhan University, 2025, 50(8): 1619–1630. doi: 10.13203/j.whugis20240501. [13] 张双成, 王铭辉, 杨娜, 等. 国产航天宏图一号SAR卫星数据干涉测量重建DEM精度验证与分析[J]. 遥感学报, 2025, 29(5): 1290–1304. doi: 10.11834/jrs.20254548.ZHANG Shuangcheng, WANG Minghui, YANG Na, et al. SAR satellite data from domestic PIESAT-1 interferometric reconstruction: DEM accuracy verification and analysis[J]. National Remote Sensing Bulletin, 2025, 29(5): 1290–1304. doi: 10.11834/jrs.20254548. [14] 赵克祥, 毕辉, 张冰尘. 基于快速阈值迭代的SAR层析成像处理方法[J]. 系统工程与电子技术, 2017, 39(5): 1019–1023. doi: 10.3969/j.issn.1001-506X.2017.05.11.ZHAO Kexiang, BI Hui, and ZHANG Bingchen. SAR tomography method based on fast iterative shrinkage-thresholding[J]. Systems Engineering and Electronics, 2017, 39(5): 1019–1023. doi: 10.3969/j.issn.1001-506X.2017.05.11. [15] 陈重华, 李楠, 完备, 等. InSAR卫星编队构形保持控制方法[J]. 测绘学报, 2022, 51(12): 2448–2454. doi: 10.11947/j.AGCS.2022.20210382.CHEN Chonghua, LI Nan, WAN Bei, et al. Formation-maintaining control strategy for InSAR satellite[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(12): 2448–2454. doi: 10.11947/j.AGCS.2022.20210382. -
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