应用联合自聚焦实现低信噪比ISAR成像平动补偿

传统逆合成孔径雷达(ISAR)平动补偿中，由于低信噪比下包络对齐无法精确实现，因此会导致后续相位自聚焦的精度受到限制．针对该问题，提出了一种包络相位联合自聚焦算法．该算法可以实现低信噪比下对ISAR目标复杂的平动分量进行补偿．利用二维ISAR图像熵作为目标函数，结合阻尼牛顿法进行最优化求解，在低信噪比下完成对平动分量的精确估计．另外，考虑到直接采样模式下，ISAR回波包络的缓变特性，对应平动分量可用有限阶多项式进行拟合．因此，利用归一化多项式拟合实现估计参数规模的简化，提高了估计精度和效率．Yak-42实测数据验证了该算法在低信噪比下的有效性和可靠性．

Translational motion compensation for ISAR imaging based on joint autofocusing under the low SNR

In conventional inverse synthetic aperture radar(ISAR),since range alignment can not be accurately performed in low signal-to-noise(SNR) environment,the subsequent phase adjustment will be limited in precision.To address this problem,a novel autofocusing method is proposed,which is capable of correcting both range shift and phase disturbance.It can compensate the translational motion in a complex form for ISAR data contaminated with strong noise.The method utilizes the entropy of the ISAR image as the optimization function,and the Damped Newton algorithm is applied to solve the problem efficiently.According to the fact that the translational motion can be usually fitted by limited-order polynomial,the normalized polynomial fitting technique is applied to enhance both the efficiency and precision of the proposal.Finally,real data of Yak-42 are used to validate the effectiveness and reliability of the proposed method under the low SNR.