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基于压缩感知的星载IFMCW SAR方位间断数据重构算法*

钟声依柳1,2, 乔明1,†, 刘云龙1, 张桐1   

  1. 1 中国科学院空天信息创新研究院,北京100190;
    2 中国科学院大学电子电气与通信工程学院,北京 100049
  • 收稿日期:2023-02-13 修回日期:2023-04-17 发布日期:2023-05-23
  • 通讯作者: E-mail:qiaoming@aircas.ac.cn
  • 基金资助:
    *国家自然科学基金(41874059)资助

Compression-sensing-based algorithm for the azimuth interrupted data reconstruction of spaceborne IFMCW SAR

ZHONG Shengyiliu1,2, QIAO Ming1, LIU Yunlong1, ZHANG Tong1   

  1. 1 Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100190,China;
    2 School of Electronic,Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100049,China
  • Received:2023-02-13 Revised:2023-04-17 Published:2023-05-23

摘要: 在星载合成孔径雷达(synthetic aperture radar, SAR)平台轻小型化发展的进程中,间断调频连续波(interrupted frequency modulated continuous wave,IFMCW)体制SAR因其具有轻重量、小体积、低功耗、低数据率的优势,是一种经济实用的体制方案。在IFMCW SAR成像上,发射模式和接收模式的交替会在合成孔径的过程中形成数据间断,这些间断将会在目标两侧引入伪峰。本文主要提出了基于压缩感知的IFMCW SAR方位向间断数据重构方法,首次将分段正交匹配追踪(stage-wise orthogonal matching pursuit, StOMP)和稀疏度自适应匹配追踪(sparsity adaptive matching pursuit, SAMP)数据重构算法运用在IFMCW方位数据重构中,解决了现有算法依赖场景稀疏度先验知识的问题。同时优化了SAMP算法结构,提升算法对IFMCW SAR数据重构的适用性。通过对IFMCW SAR仿真点目标回波数据和地面真实场景回波数据处理,验证了所提两种算法在无场景稀疏度情况下的有效性。最后在处理速度和重建效果两个方面将StOMP和改进SAMP与原算法做了对比,展现了不同处理场景下改进SAMP算法对稀疏场景处理的良好性能和StOMP对复杂场景的处理优势。

关键词: 间断调频连续波, 合成孔径雷达, 压缩感知, 数据重构

Abstract: The interrupted frequency modulated continuous wave (IFMCW) SAR is an economical and practical solution for the development of light-duty and miniature spaceborne synthetic aperture radar (SAR) systems due to its light weight, small size, low power consumption, and low data rate. However, data interruptions occurs in the received azimuthal signals due to the alternation between the “transmit” and “receive” modes of IFMCW SAR, which lead to pseudo-peaks on both sides of the target in the imaging result. In this paper, a compression sensing-based azimuthal interrupted data reconstruction method for IFMCW SAR is proposed, employing stage-wise orthogonal matching pursuit (StOMP) and sparsity adaptive matching pursuit (SAMP) to azimuthal data reconstruction for the first time. The proposed method solves the problem of the existing algorithm, which requires sparsity as a priori knowledge. In addition, the method optimizes the SAMP structure, which improves the applicability of the algorithm on IFMCW SAR's data reconstruction. The effectiveness of the proposed algorithms is verified by processing both simulated point target and ground truth echo data with unknown sparsity. Furthermore, the processing speed and reconstruction effect are compared with the original algorithm, revealing the good performance of the improved SAMP algorithm in sparse scenarios and the advantage of StOMP in complex scenarios.

Key words: interrupted frequency modulated continuous wave, synthetic aperture radar, compressed sensing, data reconstruction

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