基于联合块稀疏模型的随机调频步进ISAR成像方法

在回波数据稀疏、低信噪比等不利条件下，利用随机调频步进信号进行ISAR成像时，成像性能将会严重下降。针对上述问题，该文在充分分析随机调频步进信号回波特性的基础上，提出利用目标距离向具有的联合块稀疏特征来获得高质量ISAR图像的新方法。首先，推导了在随机调频步进信号发射波形条件下目标回波信号的联合块稀疏成像模型并分析了该模型特征；其次，提出了联合块稀疏正交匹配追踪稀疏重构算法(JBOMP)实现对模型的求解。该算法利用ISAR回波信号具有的块稀疏以及联合稀疏等先验信息，因此在低量测值、低信噪比条件下的ISAR成像性能得到了增强。所提算法还可以实现对多维信号的联合处理，且具有较快的运算速度。理论分析与仿真实验均验证了所提方法的有效性。

Random Chirp Frequency-stepped Signal ISAR Imaging Algorithm Based on Joint Block-sparse Model

Under the condition of lack of echo data and low SNR, the ISAR imaging performance is greatly reduced by using Random Chirp Frequency-Stepped (RCFS) signal. To solve the above problems, based on fully analyzing the echo characteristics of the random chirp frequency-stepped signal, a new method of obtaining high quality ISAR images is proposed using the joint sparse feature of the target range dimension. First, a joint block sparse imaging model of the target echo signal under the condition of random chirp frequency-stepped signal is derived and the characteristics of the model are analyzed. Secondly, a Joint Block sparse Orthogonal Matching Pursuit (JBOMP) algorithm is proposed for solving the model. The algorithm utilizes the sparse information and the joint sparse information of the ISAR echo. Therefore, the ISAR imaging performance is enhanced under the condition of low measurement and low SNR. The proposed algorithm also can achieve joint processing of multidimensional signals and has a faster operation speed. Both theoretical analysis and simulation experiments verify the effectiveness of the proposed method.