Turn off MathJax
Article Contents
HUANG Jiajie, DONG Jingwen, LIU Chenyu, et al. Generalized relax-based multiband fusion method for SAR using an SASC model[J]. Journal of Radars, in press. doi: 10.12000/JR26036
Citation: HUANG Jiajie, DONG Jingwen, LIU Chenyu, et al. Generalized relax-based multiband fusion method for SAR using an SASC model[J]. Journal of Radars, in press. doi: 10.12000/JR26036

Generalized RELAX-Based Multiband Fusion Method for SAR Using an SASC Model

DOI: 10.12000/JR26036 CSTR: 32380.14.JR26036
Funds:  The Major Scientific Research Instruments Development Project of the National Natural Science Foundation of China (No.62327806), The Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0870203, XDB0870200, XDB0870000).
More Information
  • Corresponding author: LI Wangzhe, wzli@mail.ie.ac.cn
  • Received Date: 2026-01-30
    Available Online: 2026-04-02
  • Multiband fusion technology is essential for enhancing radar image resolution by overcoming the hardware bandwidth limits of radar systems. Compared with nonparametric approaches, parametric methods based on scattering models offer notable advantages in noise suppression and super-resolution imaging. However, models based on the geometric theory of diffraction (GTD) are inherently limited for analyzing scatterers with continuous structures, as GTD is an asymptotic high-frequency method suited primarily for discrete scattering centers. Consequently, it fails to adequately characterize the frequency response of such continuous scatterers. To address this issue, a multiband fusion method tailored for targets that can be sparsely represented by strong scattering centers is proposed. First, a simplified attributed scattering center (SASC) model is constructed, which improves the characterization of scattering properties by incorporating the influence of the scatterer length on the frequency spectrum. Second, to address the model order estimation problem, a modified maximum singular value difference criterion is introduced to robustly estimate the model order. Building on this, a generalized RELAX -based algorithm is designed to achieve high-precision parameter estimation for the SASC model, thereby enabling effective fusion of multiband signals. Experimental results demonstrate that the proposed algorithm achieves a 6.7-fold improvement in resolution relative to the single sub-band case, while preserving the clarity and integrity of the target structure.

     

  • loading
  • [1]
    SUWA K and IWAMOTO M. A two-dimensional bandwidth extrapolation technique for polarimetric synthetic aperture radar images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(1): 45–54. doi: 10.1109/TGRS.2006.885406.
    [2]
    ZOU Yongqiang, GAO Xunzhang, LI Xiang, et al. A matrix pencil algorithm based multiband iterative fusion imaging method[J]. Scientific Reports, 2016, 6(1): 19440. doi: 10.1038/srep19440.
    [3]
    WANG Jianping, AUBRY P, and YAROVOY A. Wavenumber-domain multiband signal fusion with matrix-pencil approach for high-resolution imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(7): 4037–4049. doi: 10.1109/TGRS.2018.2821001.
    [4]
    CUOMO K M, PION J E, and MAYHAN J T. Ultrawide-band coherent processing[J]. IEEE Transactions on Antennas and Propagation, 1999, 47(6): 1094–1107. doi: 10.1109/8.777137.
    [5]
    NAISHADHAM K and PIOU J E. A robust state space model for the characterization of extended returns in radar target signatures[J]. IEEE Transactions on Antennas and Propagation, 2008, 56(6): 1742–1751. doi: 10.1109/TAP.2008.916932.
    [6]
    GUHA S, BATHELT A, CONDE M H, et al. Radar band fusion using frame-based compressed sensing[J]. IEEE Journal of Selected Topics in Signal Processing, 2023, 17(2): 403–415. doi: 10.1109/JSTSP.2022.3220403.
    [7]
    JIANG Wen, HUANG Jiajie, and LI Wangzhe. A novel multiband fusion method based on a small multiband-measurement matrix and a nonconvex Log-Sum regularization[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 3500805. doi: 10.1109/LGRS.2023.3237868.
    [8]
    SONG Shaoqiu, DAI Yongpeng, SONG Yongping, et al. Efficient near-field radar microwave imaging based on joint constraints of low-rank and structured sparsity at low SNR[J]. IEEE Transactions on Microwave Theory and Techniques, 2025, 73(5): 2962–2977. doi: 10.1109/TMTT.2024.3479189.
    [9]
    ZHANG Ying, WANG Tingjing, ZHAO Huapeng, et al. Multiple radar subbands fusion algorithm based on support vector regression in complex noise environment[J]. IEEE Transactions on Antennas and Propagation, 2018, 66(1): 381–392. doi: 10.1109/TAP.2017.2769135.
    [10]
    ZHOU Feng and BAI Xueru. High-resolution sparse subband imaging based on Bayesian learning with hierarchical priors[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(8): 4568–4580. doi: 10.1109/TGRS.2018.2827072.
    [11]
    HU Pengjiang, XU Shiyou, WU Wenzhen, et al. Sparse subband ISAR imaging based on autoregressive model and smoothed l0 algorithm[J]. IEEE Sensors Journal, 2018, 18(22): 9315–9323. doi: 10.1109/JSEN.2018.2869832.
    [12]
    WU Kejiang, CUI Wei, and XU Xiaojian. Superresolution radar imaging via peak search and compressed sensing[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4024805. doi: 10.1109/LGRS.2022.3184067.
    [13]
    ZHANG Huanhuan and CHEN Rushan. Coherent processing and superresolution technique of multi-band radar data based on fast sparse Bayesian learning algorithm[J]. IEEE Transactions on Antennas and Propagation, 2014, 62(12): 6217–6227. doi: 10.1109/TAP.2014.2361158.
    [14]
    HAI Yu, WU Junjie, MA Yuxin, et al. Microwave photonic radar lost bandwidth spectrum recovery algorithm based on improved TSPN-ADMM-Net[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5210415. doi: 10.1109/TGRS.2023.3286888.
    [15]
    HAI Yu, WU Junjie, PU Wei, et al. Joint optimization of spectrum recovery and target scattering parameter estimation in microwave photonic radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(6): 8100–8117. doi: 10.1109/TAES.2024.3424788.
    [16]
    JIANG Wen, LIU Jianwei, YANG Jiyao, et al. A novel multiband fusion method based on a modified RELAX algorithm for high-resolution and anti-non-Gaussian colored clutter microwave imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5105312. doi: 10.1109/TGRS.2021.3109724.
    [17]
    HUANG Jiajie, JIANG Wen, LIU Jianwei, et al. A resolution-improving method for multiband imaging based on an extrapolated RELAX algorithm[J]. Remote Sensing, 2024, 16(23): 4446. doi: 10.3390/rs16234446.
    [18]
    GERRY M J, POTTER L C, GUPTA I J, et al. A parametric model for synthetic aperture radar measurements[J]. IEEE Transactions on Antennas and Propagation, 1999, 47(7): 1179–1188. doi: 10.1109/8.785750.
    [19]
    DUAN Jia, ZHANG Lei, XING Mengdao, et al. Polarimetric target decomposition based on attributed scattering center model for synthetic aperture radar targets[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(12): 2095–2099. doi: 10.1109/LGRS.2014.2320053.
    [20]
    YANG Dongwen, NI Wei, DU Lan, et al. Efficient attributed scatter center extraction based on image-domain sparse representation[J]. IEEE Transactions on Signal Processing, 2020, 68: 4368–4381. doi: 10.1109/TSP.2020.3011332.
    [21]
    JIANG Wen. Research on reconstruction-large-bandwidth ISAR imaging method based on multiband scattering center extraction[D]. [Ph.D. dissertation], Shanghai Jiao Tong University, 2023: 47–66.
    [22]
    AKAIKE H. A new look at the statistical model identification[J]. IEEE Transactions on Automatic Control, 1974, 19(6): 716–723. doi: 10.1109/TAC.1974.1100705.
    [23]
    RISSANEN J. Modeling by shortest data description[J]. Automatica, 1978, 14(5): 465–471. doi: 10.1016/0005-1098(78)90005-5.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Article views(31) PDF downloads(6) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint