基于稀疏采样的调频率估计与多目标ISAR成像

针对目前多目标成像中关于信号调频斜率估计的算法存在着采样频率过大,以及短时条件下精度不高的问题,结合压缩感知理论,提出了一种基于稀疏采样的回波信号调频率估计与多目标ISAR成像方法。首先,根据目标回波信号的特点构造超完备稀疏基,将信号投影到该稀疏基上,利用高斯随机矩阵对分解后的信号进行欠采样,采用FOCUSS稀疏重构算法,精确提取出信号的调频斜率;然后,利用估计出的调频斜率对多目标回波信号中各个目标的回波分量进行分离和补偿;最后,基于稀疏采样对各个单目标分别进行成像。仿真实验验证了该方法的可行性和有效性。

A Novel Algorithm of Chirp Rate Estimating and ISAR Imaging of Multiple Targets Based on Sparse Sampling

In view of the disadvantages of sampling at a high rate and long time duration in the current methods of multiple targets imaging, a novel algorithm of chirp rate estimating and Inverse Synthetic Aperture Radar (ISAR) imaging of Multiple Targets is proposed, combined with Compressive Sensing theory based on sparse sampling. Firstly, a redundancy sparse dictionary is established according to the characteristic of echo signals, the echo signals are decomposed into the sparse dictionary by utilizing Gauss random measurement matrices and focal undetermined system solver (FOCUSS) method to recover the imaging, and estimate the chirp rate accurately. Secondly, the echo separation and the motion compensation are done with the chirp rate estimated. Finally, obtaining the image of each target based on sparse sampling. And the results show that the method is feasible and valid.