Near-field 3D imaging approach combining MJSR and FGG-NUFFT
Near-field 3D imaging approach combining MJSR and FGG-NUFFT
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结合 MJSR 和 FGG-NUFFT 的近场 3D 成像方法
DOI：
10.21629/jsee.2019.06.06
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发表时间：
2019
影响因子：
2.1
通讯作者：
Li Qing
中科院分区：
文献类型：
--
作者：
Wang Shuzhen;Fang Yang;Zhang Jin'gang;Luo Mingshi;Li Qing
将压缩感知（CS）理论与匹配滤波（MF）技术完美结合，提出了一种结合多通道联合稀疏恢复（MJSR）和快速高斯网格非均匀快速傅立叶变换（FGGNUFFT）的近场三维成像方法。该方法具有高精度、高效率的优点：采用多通道联合稀疏约束，改善了单通道成像算法恢复的图像不一定具有相同的散射中心位置的问题;结合MF和FGG-NUFFT构造CS字典，提高了成像效率和内存需求。首先，结合MF成像方法，建立了联合稀疏恢复的近场三维成像模型。其次，采用FGG-NUFFT和逆FGG-NUFFT代替MF成像方法中的插值和傅立叶变换，并根据传统成像过程构造高精度、高效率的传感矩阵。第三，采用改进的可分离替代泛函（SSF）优化算法，仅需矩阵和向量相乘，即可实现快速成像恢复。最后，利用水平和俯仰干涉相位信息获得近场目标的三维成像。本文包含两种成像模型，唯一不同的是逆合成孔径雷达（ISAR）成像中采用的子孔径方法。与传统的CS成像方法相比，该方法在每次迭代中都包含了正变换和逆变换，提高了重建质量。实验结果表明，该方法使成像精度提高了约O（10），成像速度加快了5倍，内存使用量减少了约O（102）。2018年8月22日收到Mandarin pt。* 通讯作者。本工作得到了国家自然科学基金项目的资助（61771369; 61775219; 61640422），中央大学基础研究基金（JB 180310），中国科学院装备研究计划（YJKYYQ 20180039）、陕西省重点研发计划（2018 SF-409; 2018 ZDXM-SF 027）、自然科学基础研究计划。
A near-field three-dimensional (3D) imaging method combining multichannel joint sparse recovery (MJSR) and fast Gaussian gridding nonuniform fast Fourier transform (FGGNUFFT) is proposed, based on a perfect combination of the compressed sensing (CS) theory and the matched filtering (MF) technique. The approach has the advantages of high precision and high efficiency: multichannel joint sparse constraint is adopted to improve the problem that the images recovered by the single channel imaging algorithms do not necessarily share the same positions of the scattering centers; the CS dictionary is constructed by combining MF and FGG-NUFFT, so as to improve the imaging efficiency and memory requirement. Firstly, a near-field 3D imaging model of joint sparse recovery is constructed by combining the MF-based imaging method. Secondly, FGG-NUFFT and reverse FGG-NUFFT are used to replace the interpolation and Fourier transform in MF-based imaging methods, and a sensing matrix with high precision and high efficiency is constructed according to the traditional imaging process. Thirdly, a fast imaging recovery is performed by using the improved separable surrogate functionals (SSF) optimization algorithm, only with matrix and vector multiplication. Finally, a 3D imagery of the near-field target is obtained by using both the horizontal and the pitching interferometric phase information. This paper contains two imaging models, the only difference is the sub-aperture method used in inverse synthetic aperture radar (ISAR) imaging. Compared to traditional CS-based imaging methods, the proposed method includes both forward transform and inverse transform in each iteration, which improves the quality of reconstruction. The experimental results show that, the proposed method improves the imaging accuracy by about O(10), accelerates the imaging speed by five times and reduces the memory usage by about O(102). Manuscript received August 22, 2018. *Corresponding author. This work was supported by the National Natural Science Foundation of China (61771369; 61775219; 61640422), the Fundamental Research Funds for the Central Universities (JB180310), the Equipment Research Program of the Chinese Academy of Sciences (YJKYYQ20180039), the Shaanxi Provincial Key R&D Program (2018SF-409; 2018ZDXM-SF027), and the Natural Science Basic Research Plan.