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TAN Zhuohang, CHEN Zeyu, TANG Haojie, et al. Fast structured sparse millimeter-wave 3d SAR imaging based on low-rank and smooth matrix completion[J]. Journal of Radars, in press. doi: 10.12000/JR25267
Citation: TAN Zhuohang, CHEN Zeyu, TANG Haojie, et al. Fast structured sparse millimeter-wave 3d SAR imaging based on low-rank and smooth matrix completion[J]. Journal of Radars, in press. doi: 10.12000/JR25267
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Fast Structured Sparse Millimeter-Wave 3D SAR Imaging Based on Low-Rank and Smooth Matrix Completion

DOI: 10.12000/JR25267 CSTR: 32380.14.JR25267
  • 1.

    College of computer science and technology, Sichuan University, Chengdu 610065, China

  • 2.

    Chengdu Aircraft industrial (Group) coporation Ltd, Chengdu 610091, China

Funds:  The National Natural Science Foundation of China (U25A20402), the National Key Research and Development Program of China (2023YFF0615800), the Sichuan Science and Technology Program (2024ZHCG0191, 2026YFHZ0220)
More Information
  • Corresponding author: LIU Yiguang, liuyg@scu.edu.cn
  • Received Date: 2025-12-11
    Available Online: 2026-01-26
    Abstract
  • The millimeter-wave (mmWave) radar is widely used in security screening, nondestructive testing, and through-the-wall imaging due to its compact size, high resolution, and strong penetration capability. High-resolution mmWave radar imaging typically requires synthetic aperture emulation, which involves dense two-dimensional spatial sampling via structured scanning on a mechanical platform. However, this process is time-consuming in practical applications. Therefore, many existing studies have focused on reconstructing echo data under sparse sampling conditions for imaging. However, most existing sparse recovery methods assume uniformly random sparse sampling or involve high computational complexity, making them difficult to apply in practical synthetic aperture radar (SAR) imaging systems. This paper proposes a fast, structured sparse, mmWave three-dimensional (3D) SAR imaging algorithm based on low-rank and smooth matrix completion (MC) to address this problem. First, the global low-rank property and local smoothness prior of echo data are analyzed within the framework of near-field mmWave SAR imaging theory. Our analysis demonstrated that structured sparse SAR data arising from missing entire rows or columns in practical scanning can be recovered. Building on this, an MC model incorporating low-rank and smoothness constraints was constructed. This MC model jointly regularizes with nuclear norm and total variation and can be solved efficiently using the alternating direction method of multipliers (ADMM). Finally, the performance of the proposed algorithm was validated through multiple simulation runs and real-world experiments. Experimental results showed that, using only 20%–30% of randomly sampled rows or columns of echo data, the proposed algorithm can achieve fast data recovery and high-resolution 3D imaging within tens of seconds.

     

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