Inverse synthetic aperture ladar (ISAL) can perform high-resolution imaging for satellites. However, due to the short wavelength of the laser, satellite microvibration will introduce space-variant vibration phase error (SVVPE) and space-invariant vibration phase error (SIVVPE) in the echoes, which seriously blurs the ISAL image. In this article, we propose a noise-robust vibration phase compensation algorithm to accurately estimate and correct these two types of vibration phase errors by frequency descent (FD) minimum entropy optimization. First, considering the characteristics of the microvibration of satellites, we establish a novel phase error model based on the Fourier series theory, which only contains low-frequency vibration components. The estimation of the phase errors is then translated into the estimation of the model’s Fourier coefficients, which can be achieved by a multidimensional minimum entropy optimization. After that, an FD method is proposed to transform the multidimensional optimization into a group of 2-D optimizations so that the proposed algorithm can achieve monotonic iterative convergence. In addition, we introduce a solution space adaptive reduction operation to reduce the computational burden when solving the 2-D minimum entropy optimizations by the genetic algorithm (GA) to obtain the global optimal solution. Finally, experiments based on the simulated data and the real measured data confirm the effectiveness of the proposed algorithm. Compared with the traditional methods, the proposed algorithm achieves higher phase error estimation accuracy and better image quality.

中文翻译：

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通过频率下降最小熵优化对卫星 ISAL 成像进行抗噪声振动相位补偿


逆合成孔径激光雷达（ISAL）可以对卫星进行高分辨率成像。然而，由于激光波长短，卫星微振动会在回波中引入空变振动相位误差（SVVPE）和空间不变振动相位误差（SIVVPE），导致ISAL图像严重模糊。在本文中，我们提出了一种抗噪声振动相位补偿算法，通过频率下降（FD）最小熵优化来准确估计和纠正这两类振动相位误差。首先，考虑卫星微振动的特点，基于傅里叶级数理论建立了一种仅包含低频振动分量的新型相位误差模型。然后将相位误差的估计转化为模型傅立叶系数的估计，这可以通过多维最小熵优化来实现。之后，提出FD方法将多维优化转化为一组二维优化，使算法能够实现单调迭代收敛。此外，我们引入了解空间自适应缩减操作，以减少通过遗传算法（GA）求解二维最小熵优化以获得全局最优解时的计算负担。最后，基于仿真数据和真实测量数据的实验验证了所提算法的有效性。与传统方法相比，该算法实现了更高的相位误差估计精度和更好的图像质量。