ID	原文	译文
18175	仿真结果表明，该文方法在稀疏采样率74.4%条件下，仍可获得与满采样成像性能相当的结果，验证了干涉处理频域稀疏方法在星载SAR 3维成像中的有效性。	Simulation results show that under the condition of sparse sampling rate of 74.4%, the imaging performance of the proposed method is still comparable to that of full sampling.
18176	采用非线性对比源反演(CSI)算法求解电磁逆散射问题时，在每次迭代过程中都涉及到求解散射场数据关于对比源和总场的微分，即Jacobi矩阵，	When using the nonlinear Contrast Source Inversion (CSI) algorithm to solve the electromagnetic inverse scattering problem, each iteration involves finding the differential of the dissolution radiation field data about the contrast source and the total field, i.e., the Jacobi matrix.
18177	该矩阵求解导致算法存在计算代价大和收敛速度慢等问题。	The solution of the matrix leads to the problem of large computational cost and slow convergence speed of the algorithm.
18178	该文在CSI框架下，采用一种基于随机平均梯度下降的对比源反演算法(SAG-CSI)代替原来的全梯度交替共轭梯度算法来重构介质目标介电常数的空间分布信息。	in this paper, a ContrastSource Inversion algorithm based on Stochastic Average Gradient descent (SAG-CSI) is used instead of the original full gradient alternating Conjugate Gradient algorithm to reconstruct the spatial distribution information of the dielectric constant of the dielectric target under the CSI framework.
18179	该方法在每次迭代中只需计算随机抽取的部分测量数据在目标函数中的梯度信息，同时目标函数对未抽中的测量数据的梯度信息保持不变，用以上两部分梯度信息共同求解出目标函数的最优值。	The method only needs to calculate the gradient information of the randomly selected part of the measurement data in the objective function in each iteration, while the objective function keeps the gradient information of the unscented measurement data, and the optimal value of the objective function is solved together with the above two parts of the gradient information.
18180	由模拟数据结果表明，该方法与传统CSI方法在成像精度相比拟的情况下，降低了计算代价并提高算法收敛速度。	The simulation results show that the proposed method reduces the computationalcost and improves the convergence speed of the algorithm when compared with the traditional CSI method.
18181	在辐射源个体识别(SEI)技术中，能量较高的主信号往往导致微弱个体特征稳定性降低，进而影响最终的个体识别效果。	In Specific Emitter Identification (SEI), the stability of individual features and final correctidentification rate are always declined due to the influence of the primary signal with high energy on theindividual features.
18182	为了解决该问题并提升辐射源个体识别性能，该文提出基于同步压缩小波变换的主信号抑制技术。	To solve the problem above, a primary signal suppression algorithm based onsynchrosqueezed wavelet transform is exploited for specific emitter identification in this paper.
18183	首先，利用静态小波变换完成对带噪信号的去噪预处理；	Firstly, adenoising method based on stationary wavelet transform is applied to preprocess the noised signal;
18184	然后，利用同步压缩小波变换完成对主信号的检测和抑制，并以均方根误差和皮尔逊相关系数为数值指标，验证算法的有效性；	Then, thedetection and suppression of the primary signal from time-frequency distribution are developed, where rootmean square error and Pearson correlation coefficient are used as numerical indicators to measure theeffectiveness of the proposed primary signal suppression algorithm;