In this letter, an efficient near-field millimeter-wave (MMW) imaging algorithm is presented for layered dielectrics based on multiple-input–multiple-output synthetic aperture radar (MIMO-SAR) with nonuniform transmitting array. First, the model of target echo under MIMO-SAR configuration is built by dyadic Green's function, and the precise spectral echo expression is obtained through fast Fourier transform (FFT) and spherical-wave decomposition. Then, the image reconstruction of sparse MIMO-SAR is divided into separate single-input–multiple-output (SIMO)-SAR imaging problems, and the target's subimage corresponding to different SIMO-SAR setup can be quickly solved by employing inverse FFT, multistep phase compensation and wavenumber integration. Finally, the fully focused MIMO-SAR reconstructed image will be obtained by coherently accumulating all the SIMO-SAR results. Simulation analysis and experimental results validate the effectiveness the proposed algorithm.

中文翻译：

---

深度神经网络：大规模 MIMO 系统中传统信道估计器的替代方案


在这封信中，提出了一种基于非均匀发射阵列的多输入多输出合成孔径雷达（MIMO-SAR）的分层电介质的高效近场毫米波（MMW）成像算法。首先，利用二进格林函数建立了MIMO-SAR配置下的目标回波模型，并通过快速傅里叶变换（FFT）和球面波分解得到精确的频谱回波表达式。然后，稀疏MIMO-SAR的图像重建被分为单独的单输入多输出（SIMO）-SAR成像问题，并且可以通过采用逆FFT、多步快速求解对应于不同SIMO-SAR设置的目标子图像。相位补偿和波数积分。最后，通过相干累加所有SIMO-SAR结果，获得完全聚焦的MIMO-SAR重建图像。仿真分析和实验结果验证了该算法的有效性。