Abstract The radar coincidence imaging (RCI) doesn’t rely on relative motion between target and radar, but its super-resolution characteristics requires a large sample size of the radiation fields. In the actual operation, transmitting too many signals at once not only requires large antenna array, but also easily causes aliasing. Thus the model of adaptive frame-iteration compressive sensing (AFCS) was proposed in this paper. Compared to the traditional antenna array, MIMO antenna array [1] transmits signals independently and ensures low enough correlations between every array element. Based on the spatial multiplexing characteristics of MIMO antenna array, in each iteration-frame the randomly selected array elements transmit incoherent signals, and the scattering coefficients of target plane can be obtained by correlation processing of the echo signal and the reference signal. Moreover, according to the distribution of scattering coefficients, we can combine frame-iteration and compressive sensing to realize super-resolution imaging. Numerical simulation results demonstrate that the proposed model is feasible.

中文翻译：

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基于自适应帧迭代压缩感知的雷达重合成像

摘要 雷达符合成像（RCI）不依赖于目标与雷达之间的相对运动，但其超分辨率特性需要大样本量的辐射场。在实际操作中，一次传输过多的信号，不仅需要很大的天线阵列，而且容易造成混叠。因此，本文提出了自适应帧迭代压缩感知（AFCS）模型。与传统天线阵列相比，MIMO天线阵列[1]独立传输信号并确保每个阵元之间的相关性足够低。基于MIMO天线阵列的空间复用特性，在每个迭代帧中随机选择的阵元发射非相干信号，通过回波信号与参考信号的相关处理，可以得到目标平面的散射系数。此外，根据散射系数的分布，我们可以结合帧迭代和压缩感知来实现超分辨率成像。数值模拟结果表明所提出的模型是可行的。