作者中心
专家审稿
责编办公
编辑办公
Time-Varying Baseline Estimation Algorithm for Small UAVs-Borne Distributed TomoSAR
-
摘要: 轻小型无人机载分布式层析合成孔径雷达(SAR)系统受限于载荷，难以搭载高精度定位定姿系统，且其飞行轨迹易受低空大气湍流影响，导致系统中存在显著的残余时变基线误差，严重降低目标三维重建精度。与飞行高度较高的常规机载重轨三维SAR系统相比，轻小型无人机载分布式层析SAR系统的飞行高度低，对时变基线误差的补偿精度要求更为苛刻。在图像信噪比较低且系统中存在较大的时变基线误差的情况下，现有时变基线估计方法难以获得稳定可靠的估计结果。针对上述问题，该文提出一种基于图像方位偏移量的两步式时变基线误差估计算法。该方法通过分块配准与曲面拟合、子孔径处理两个步骤，依次估计时变基线误差，并采用迭代策略进一步提升估计精度。基于C波段轻小型无人机载分布式层析SAR的实测数据验证结果表明，与EMSP方法相比，所提方法在多数通道上可显著降低子孔径干涉相位差分的均方根值，有效提升通道间相干性，所提方法获得的点云高程向标准差由5.16 m降低至1.33 m，建筑目标的高度重建误差优于0.5 m，验证了所提方法的有效性与优势。Abstract: Small unmanned aerial vehicle (UAV)–borne distributed tomographic synthetic aperture radar (TomoSAR) systems exhibit remarkable residual time-varying baseline errors due to the limited precision of the position and orientation system on small UAV platforms. These errors critically degrade the performance of three-dimensional (3D) target reconstruction. Compared with airborne repeat-pass 3D synthetic aperture radar (SAR), distributed TomoSAR mounted on small UAVs imposes stricter compensation accuracy requirements for time-varying baseline errors because of the altitude constraints of the carrying platform. Under the conditions of low signal-to-noise ratio and substantial time-varying baseline errors, existing estimation methods often fail to provide stable and reliable results. In this paper, a two-step time-varying baseline error estimation method based on image azimuth displacement is proposed. The method sequentially estimates the low-frequency component through the co-registration of the master and slave images and the high-frequency component using a multisquint algorithm. Iterative refinement is applied to enhance estimation accuracy. The experimental results obtained from real C-band small UAV-borne distributed TomoSAR data demonstrate that, compared with the enhanced multisquint processing method, the proposed method considerably reduces the root mean square of differential interferometric phases across most channels, thereby effectively improving interchannel coherence. In addition, the elevation-direction standard deviation of the reconstructed point cloud is reduced from 5.16 to 1.33 m, and the height reconstruction error of building targets is less than 0.5 m, validating the effectiveness and superiority of the proposed method.
-
图 3 目标正确重建概率与站点位置测量误差的关系
Figure 3. Probability of correct reconstruction vs. site location measurement errors
图 4 不同子孔径带宽下，EMSP方法的处理效果图
Figure 4. Processing results of the EMSP method under different sub-aperture bandwidth
图 6 方位向偏移量与时变基线误差之间的关系
Figure 6. Geometric relationship between azimuth displacement and the time-varying baseline-errors
图 10 时变基线误差补偿前后主图像(CH7)与其他辅图像间的干涉相位图
Figure 10. Interferometric phase between the master (CH7) and other slave images before and after time-varying baseline error compensation
图 11 不同方法估计时变基线误差后相干系数变化曲线图
Figure 11. Coherence coefficient variation curves after estimating the time-varying baseline error using different methods
图 12 不同方法估计时变基线误差时干涉相位差分值的RMS变化曲线图
Figure 12. RMS variation curves of interferometric phase differences during time-varying baseline error estimation using different methods
图 13 线性和常数项基线误差校正后干涉相位图
Figure 13. Interferometric phase map after linear and constant baseline error correction
表 1 仿真实验主要参数
Table 1. Main parameters of the simulation experiment
参数 数值 载波波段 C 收发模式 2T4R 平台飞行高度(m) 300, 1000 ,3000 中心下视角(°) 60 点目标高度(m) 5, 10, 15 最长基线(m) 21 基线倾角(°) 0 
下载: 导出CSV
表 2 分布式飞行实验主要系统参数
Table 2. System parameters of the C-Band distributed TomoSAR experiment
参数 数值 载波波段 C 距离向分辨率(m) 0.38 方位向分辨率(m) 0.10 下视角(°) 55 飞行速度(m/s) 3.00 最长高程向基线(m) 10.26 高程向瑞利分辨率(m) 1.50
下载: 导出CSV
-
[1] ZHU Xiaoxiang and BAMLER R. Superresolving SAR tomography for multidimensional imaging of urban areas: Compressive sensing-based TomoSAR inversion[J]. IEEE Signal Processing Magazine, 2014, 31(4): 51–58. doi: 10.1109/MSP.2014.2312098. [2] LIN Yuqing, QIU Xiaolan, and DING Chibiao. TomoSAR three-dimensional image restoration in urban area by multipath exploitation[C]. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024: 2031–2035. doi: 10.1109/IGARSS53475.2024.10642195. [3] WANG Yan, DING Zegang, LI Linghao, et al. First demonstration of single-pass distributed SAR tomographic imaging with a P-band UAV SAR prototype[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5238618. doi: 10.1109/TGRS.2022.3221859. [4] BUDILLON A, FERRAIOLI G, JOHNSY A C, et al. TomoSAR application for early warning in infrastructure health monitoring[C]. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019: 3621–3624. doi: 10.1109/IGARSS.2019.8898674. [5] YANG Wenyu, VITALE S, AGHABABAEI H, et al. A deep learning solution for height inversion on forested areas using single and dual polarimetric TomoSAR[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 2505605. doi: 10.1109/LGRS.2023.3322782. [6] PARDINI M, CAZCARRA-BES V, and PAPATHANASSIOU K P. TomoSAR mapping of 3D forest structure: Contributions of L-band configurations[J]. Remote Sensing, 2021, 13(12): 2255. doi: 10.3390/rs13122255. [7] SHAHZAD M, MAURER M, FRAUNDORFER F, et al. Buildings detection in VHR SAR images using fully convolution neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(2): 1100–1116. doi: 10.1109/TGRS.2018.2864716. [8] SHAKHATREH H, SAWALMEH A H, AL-FUQAHA A, et al. Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges[J]. IEEE Access, 2019, 7: 48572–48634. doi: 10.1109/ACCESS.2019.2909530. [9] WANG Huan, LIU Yunlong, LI Yanlei, et al. Improved real-time SPGA algorithm and hardware processing architecture for small UAVs[J]. Remote Sensing, 2025, 17(13): 2232. doi: 10.3390/rs17132232. [10] BRANCATO V, JÄGER M, SCHEIBER R, et al. A motion compensation strategy for airborne repeat-pass SAR data[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(10): 1580–1584. doi: 10.1109/LGRS.2018.2848596. [11] REIGBER A. Correction of residual motion errors in airborne SAR interferometry[J]. Electronics Letters, 2001, 37(17): 1083–1084. doi: 10.1049/el:20010724. [12] REIGBER A and SCHEIBER R. Airborne differential SAR interferometry: First results at L-band[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(6): 1516–1520. doi: 10.1109/TGRS.2003.814610. [13] REIGBER A, PRATS P, and MALLORQUI J J. Refined estimation of time-varying baseline errors in airborne SAR interferometry[J]. IEEE Geoscience and Remote Sensing Letters, 2006, 3(1): 145–149. doi: 10.1109/LGRS.2005.860482. [14] ZHONG Xuelian, XIANG Maosheng, YUE Huanyin, et al. Algorithm on the estimation of residual motion errors in airborne SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 1311–1323. doi: 10.1109/TGRS.2013.2249665. [15] CAO Ning, LEE H, ZAUGG E, et al. Estimation of residual motion errors in airborne SAR interferometry based on time-domain backprojection and multisquint techniques[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4): 2397–2407. doi: 10.1109/TGRS.2017.2779852. [16] WANG Huiqiang, FU Haiqiang, ZHU Jianjun, et al. Correction of time-varying baseline errors based on multibaseline airborne interferometric data without high-precision DEMs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(11): 9307–9318. doi: 10.1109/TGRS.2020.3041056. [17] DE MACEDO K A C, SCHEIBER R, and MOREIRA A. An autofocus approach for residual motion errors with application to airborne repeat-pass SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(10): 3151–3162. doi: 10.1109/TGRS.2008.924004. [18] LUOMEI Yixiang and XU Feng. Real-time implementation of segmental aperture imaging algorithm for multirotor-borne minisar[C]. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022: 4529–4532. doi: 10.1109/IGARSS46834.2022.9883374. [19] FU Haiqiang, ZHU Jianjun, WANG Changcheng, et al. A wavelet decomposition and polynomial fitting-based method for the estimation of time-varying residual motion error in airborne interferometric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(1): 49–59. doi: 10.1109/TGRS.2017.2727076. [20] TEBALDINI S, ROCCA F, MARIOTTI D’ALESSANDRO M, et al. Phase calibration of airborne tomographic SAR data via phase center double localization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(3): 1775–1792. doi: 10.1109/TGRS.2015.2488358. [21] ZENG Guobing, XU Huaping, WANG Yuan, et al. A novel method for airborne SAR tomography baseline error correction driven by small baseline interferometric phase[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5225013. doi: 10.1109/TGRS.2024.3478055. [22] IMPERATORE P and FORNARO G. Joint phase-screen estimation in airborne multibaseline SAR tomography data processing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 4412614. doi: 10.1109/TGRS.2024.3446186. [23] ZHU Xiaoxiang and BAMLER R. Demonstration of super-resolution for tomographic SAR imaging in urban environment[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(8): 3150–3157. doi: 10.1109/TGRS.2011.2177843. [24] BUCKREUSS S. Motion compensation for airborne SAR based on inertial data, RDM and GPS[C]. IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 1994: 1971–1973. doi: 10.1109/IGARSS.1994.399626. [25] CHEN Jianlai, XING Mengdao, YU Hanwen, et al. Motion compensation/autofocus in airborne synthetic aperture radar: A review[J]. IEEE Geoscience and Remote Sensing Magazine, 2022, 10(1): 185–206. doi: 10.1109/MGRS.2021.3113982. [26] 张福博. 阵列干涉SAR三维重建信号处理技术研究[D]. [博士论文], 中国科学院大学, 2015: 34–37.ZHANG Fubo. Research on Signal Processing of 3-D reconstruction in Linear Array Synthetic Aperture Radar Interferometry[D]. [Ph.D. dissertation], The University of Chinese Academy of Sciences, 2015: 34-37. [27] REIGBER A and PAPATHANASSIOU K P. Correction of residual motion errors in airborne repeat-pass interferometry[C]. IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217), Sydney, Australia, 2001: 3077–3079. doi: 10.1109/IGARSS.2001.978260. [28] CHENG Kexin and DONG Youqiang. An image compensation-based range-Doppler model for SAR high-precision positioning[J]. Applied Sciences, 2024, 14(19): 8829. doi: 10.3390/app14198829. [29] XU Bing, LI Zhiwei, WANG Qijie, et al. A refined strategy for removing composite errors of SAR interferogram[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(1): 143–147. doi: 10.1109/LGRS.2013.2250903. [30] 邓袁. 机载重轨干涉SAR高精度配准算法研究[D]. [硕士论文], 中国科学院大学, 2014: 5–7.DENG Yuan. Research on Highly Precise Registration Algorithm of Airborne Repeat-Pass Interferometric SAR[D]. [Master dissertation], The University of Chinese Academy of Sciences, 2014: 5-7. [31] PRATS P, SCHEIBER R, REIGBER A, et al. Estimation of the surface velocity field of the Aletsch glacier using multibaseline airborne SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(2): 419–430. doi: 10.1109/TGRS.2008.2004277. [32] ZHU Xiaoxiang and BAMLER R. Tomographic SAR inversion by L1 -norm regularization—the compressive sensing approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(10): 3839–3846. doi: 10.1109/TGRS.2010.2048117. -
图(14) / 表(2)
计量
- 文章访问数:
- HTML全文浏览量:
- PDF下载量:
- 被引次数: 0
