中文摘要：

针对低信噪比短相干处理时间（CPI）的逆合成孔径雷达（ISAR）成像及应用,提出一种有效的ISAR横向定标算法.基于ISAR图像的稀疏特征,该方法首先利用加权压缩感知实现超分辨成像,有效地抑制噪声并保证强散射点的恢复精度.然后,根据二维傅里叶变换及极坐标变换的特性,利用极坐标图像间的相关性对转动角速度进行初估计.最后,利用加权压缩感知对相关函数峰值位置进行精确估计,提高了估计精度和效率,从而实现ISAR图像的横向定标.通过仿真和实测数据处理验证了该方法的可行性和有效性.

英文摘要：

This paper proposes a new algorithm for solving cross-range scaling for the inverse synthetic aperture radar（ISAR） imaging during a short Coherent Processing Interval （CPI） under a low Signal Noise Ratio （SNR）.Based on the sparsity characteristic of the ISAR image,a Weighted Compressive Sensing （WCS） procedure is applied to generate high-resolution images,which can encourage signal components while suppressing noise.Then on the basis of the characteristics of 2-D Fourier transform （2-D FFT） and polar mapping,the Rotation Angle Velocity（RAV）initial estimation is realized by the correlation between two polar images.Finally,the maximum correlation position is found by using WCS,improving the estimation precision and efficiency of RAV.The rescaled ISAR image can be implemented.Both simulated and real-measured data confirm the feasibility and effectiveness of the proposed algorithm.