Combined with sparse SIMO radar array in space domain, the two dimensional sparsity model for radar imaging with high resolution is established in this paper with frequency-stepped chirp signal (FSCS). First, a velocity estimation method for moving targets is introduced for this model based on image entropy criteria, and the accurate velocity estimation is implemented by simple zero-padding for low dimensional data. Second, in combination of the Compressed Sensing (CS) theory, the effective measurement matrix, the sparsity transform matrix and the reconstruction algorithm are designed to obtain the high-resolution range profile (HRRP) , then a phase remain character based new imaging method is proposed for SIMO high-resolution sparse radar by two dimensional sparsity processing in the frequency and the space domains. The model can reduce not only the number of sub-pulses in FSCS burst, but also reduce the number of receivers of SIMO high-resolution radar, in the meanwhile, the high quality HRRP and two dimensional images of targets can be achieved. Effectiveness and robustness of the method are proved by the simulation results.%利用频域稀疏的线性调频步进信号(FSCS)作为雷达发射信号,并结合空域稀疏的SIMO雷达阵列来构建二维稀疏的高分辨雷达成像模型.针对该稀疏模型,首先通过对低维数据简单补零处理,然后利用图像熵准则完成对运动目标速度的有效估计.在此基础上,结合压缩感知理论,构造有效的观测矩阵、稀疏变换矩阵以及重构算法,获得目标高分辨距离像(HRRP),进一步提出基于保相性的频域空域二维稀疏SIMO高分辨雷达成像方法.该方法可以大幅减少FSCS脉冲串的子脉冲个数,大幅减少SIMO高分辨雷达接收天线阵元个数,并获得高质量的HRRP和目标二维像.仿真实验验证了本文方法的有效性和鲁棒性.
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