低信噪比下的二维联合线性布雷格曼迭代快速超分辨成像算法

发布时间：2018-03-17 15:55

本文选题：逆合成孔径雷达　切入点：压缩感知　出处：《物理学报》2016年03期 　论文类型：期刊论文

【摘要】：针对实际逆合成孔径雷达(ISAR)成像时带宽有限、方位孔径稀疏的小角度回波数据条件下,常规算法的成像分辨率不高等问题,基于压缩感知理论,提出了一种低信噪比条件下的二维联合布雷格曼迭代快速ISAR超分辨成像算法.首先,将雷达回波构建为距离频域-方位多普勒域的二维稀疏表示模型,在此基础上,将二维超分辨成像问题转换为二维联合压缩感知的稀疏重构问题;其次,为了避免重构时向量化操作带来的复杂度,提出了二维联合布雷格曼迭代算法,为实现快速重构,将加权残量迭代、估计停滞步长与感知矩阵条件数优化三种加快收敛速度的思想相结合,既利用了布雷格曼迭代在低信噪比条件下的重构能力又能保证快速成像.最后仿真实验结果表明在欠采样和低信噪比条件下本文算法能够缩短成像时间,且具备更好的噪声鲁棒性.
[Abstract]:In view of the limited bandwidth and sparse azimuth aperture of small angle echo data in real inverse synthetic Aperture Radar (ISAR) imaging, the resolution of conventional algorithms is not high, which is based on the theory of compression sensing. In this paper, a two-dimensional combined Bregman iterative ISAR super-resolution imaging algorithm with low signal-to-noise ratio is proposed. Firstly, the radar echo is constructed as a two-dimensional sparse representation model in the range frequency-azimuth Doppler domain. In order to avoid the complexity of vectorization operation, a two-dimensional joint Bragman iterative algorithm is proposed to achieve fast reconstruction, in order to avoid the complexity of the vectorization operation, the two-dimensional super-resolution imaging problem is converted to the sparse reconstruction problem of two-dimensional joint compression sensing. The weighted residuals iteration, the estimation of stagnation step size and the optimization of the condition number of perceptual matrix are combined to accelerate the convergence rate. The reconstruction ability of Bregman iteration under low signal-to-noise ratio (SNR) is utilized and the fast imaging is guaranteed. Finally, the simulation results show that the proposed algorithm can shorten the imaging time under the condition of under-sampling and low signal-to-noise ratio (SNR). And has better noise robustness.
【作者单位】：空军预警学院三系;
【基金】：国家自然科学基金(批准号:61179014)资助的课题~~
【分类号】：TN957.52

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