稀疏孔径下的运动补偿及快速超分辨成像方法

针对稀疏孔径条件下目标运动补偿难和方位稀疏成像算法效率低、分辨率差等问题，本文提出了一种稀疏孔径下的运动补偿和快速超分辨成像方法.首先，通过将运动补偿问题转换为距离频域内的多参数估计问题，基于黄金分割法实现参数的快速估计后同时实现包络对齐和相位校正，从而完成运动补偿；其次，针对补偿后不同距离单元ISAR回波的特征，为实现快速的方位成像，本文提出矩阵形式的Nesterov线性Bregman迭代算法（Matrix form of Nesterov Linearized Bregman Iteration，MNLBI）算法，分析了该算法的基本迭代格式，讨论了加快收敛的原因，并详细分析了该算法的运算量，仿真与实测数据结果验证了本文方法的有效性.

Research on Motion Compensation and Fast Super Resolution Imaging Method Under Sparse Aperture Condition

In inverse synthetic aperture radar,the difficulty of motion compensation,the low imaging efficiency and resolution of sparse apertures for non-cooperate targets is a challenge problem.To solve the problem,a novel motion compensation and fast imaging method is proposed in this paper.First,the motion compensation is converted into a multi-parameters estimation problem.In order to accomplish the motion compensation,golden selection search (GSS) is adopted to estimate the multi-parameters.Second,the ISAR echoes' feature changes as range cell changing.To realize azimuth imaging efficiently,a matrix form of Nesterov linearized Bregman iteration (MNLBI) algorithm is proposed and the basic iteration scheme is presented as well.The method to speed up convergence of MNLBI is also given.Finally,the robustness to noise and computation is analyzed.The simulation and real data results show the effectiveness of the proposed method.