基于深度展开模型的毫米波稀疏成像算法

针对传统压缩感知算法的高计算成本问题，从稀疏信号的恢复角度出发，提出一种基于深度展开模型的稀疏成像算法。首先构建复数稀疏重建网络VAMP-Net，在该网络中，复数形式的降采样回波信号被拆分成实部和虚部作为输入，接着代入到基于VAMP算法的迭代块中，最后通过卷积神经模块进行最优非线性稀疏变换，得到恢复的实部和虚部信号，两者进行合并后得到恢复的目标图像。对于所提算法，采用人工合成的数据集在不同目标密集程度、迭代次数和噪声环境下进行仿真实验，并与传统的迭代收缩阈值算法和深度学习重构算法进行比较。再使用不同程度稀疏性的数据进行实测验证。实验结果表明，该算法重构的图像在NMSE、TBR、重构耗时以及内存使用上有更好的表现。

Millimeter wave sparse imaging algorithm based on depth expansion model

Aiming at the high computational cost of traditional compressed sensing algorithms, this paper proposed a sparse imaging algorithm based on depth expansion model from the perspective of sparse signal recovery. Firstly, it constructed a complex sparse reconstruction network VAMP-Net. In VAMP-Net, it divided complex regressive echo signal into real part and imaginary part as input. Secondly, it substituted the input into the iterative block based on VAMP algorithm. Finally, it carried out the optimal nonlinear sparse transformation by convolutional neural module to obtain the recovered real part and imaginary part signals, and then merged them to obtain the restored target image. As for the proposed algorithm, this paper used artificial data sets to conduct simulation experiments under different target density, iteration times and noise environment, and compared with the traditional iterative shrinkage threshold algorithm and deep learning reconstruction algorithm. Then it used the measured data with different sparsity for field measurement verification. Experimental results show that the image reconstructed by this algorithm has better performance in NMSE, TBR, reconstruction speed and memory usage.