散射区域加权压缩感知ISAR成像

压缩感知(compressive sensing，CS)理论为少量脉冲条件下实现高分辨逆合成孔径雷达(inverse synthetic aperture radar， ISAR)成像提供了新方法。然而由于CS的噪声敏感性，其成像易受到噪声污染；另外，少量脉冲条件下很难保证噪声参数估计精度，这进一步加剧了ISAR成像污染。针对这一问题，提出一种散射区域加权CS ISAR成像算法，利用目标散射区域信息对冗余字典中的基函数进行加权，修正CS重建算法以抑制噪声散斑。为提高噪声参数估计精度，对回波采样建立子序列矩阵，提出矩阵扰动理论噪声参数估计方法。实验结果表明,所提方法能够有效抑制噪声影响，提高低信噪比和少量脉冲条件下ISAR成像质量。

Scattering region weighting compressive sensing ISAR imaging

Compressive sensing (CS)theory provides a new approach for inverse synthetic aperture radar(ISAR) to realize high-resolution imaging under very limited number of pulses. However, due to the noise sensitivity of CS, the quality of ISAR images suffers from noise contamination. In addition, in noise circumstance, it is hard to obtain accurate noise parameters estimation in the case of few pulses. This further exacerbates ISAR image contamination. To deal with this problem, a scattering region weighting compressive sensing ISAR imaging method is proposed. With the target region information, the weighting coefficients are determined for the basis function in the redundant dictionary. Then the CS reconstruction algorithm is modified with the target information weighting to suppress noise speckles. To improve the noise level estimation precision, the sub-sequence matrix is established from return samples,and then the matrix perturbation theory is performed to estimate the noise parameters. Experimental results indicate that the proposed method can effectively reduce noise and improve the image quality in the case of low signal-noise ratio and very limited number of pulses case.