This paper introduces a method based on a deep neural network (DNN) that is perfectly capable of processing radar data from extremely thinned radar apertures. The proposed DNN processing can provide both aliasing-free radar imaging and super-resolution. The results are validated by measuring the detection performance on realistic simulation data and by evaluating the Point-Spread-function (PSF) and the target-separation performance on measured point-like targets. Also, a qualitative evaluation of a typical automotive scene is conducted. It is shown that this approach can outperform state-of-the-art subspace algorithms and also other existing machine learning solutions. The presented results suggest that machine learning approaches trained with sufficiently sophisticated virtual input data are a very promising alternative to compressed sensing and subspace approaches in radar signal processing. The key to this performance is that the DNN is trained using realistic simulation data that perfectly mimic a given sparse antenna radar array hardware as the input. As ground truth, ultra-high resolution data from an enhanced virtual radar are simulated. Contrary to other work, the DNN utilizes the complete radar cube and not only the antenna channel information at certain range-Doppler detections. After training, the proposed DNN is capable of sidelobe- and ambiguity-free imaging. It simultaneously delivers nearly the same resolution and image quality as would be achieved with a fully occupied array.

翻译：本文介绍了一种基于深度神经网络(DNN)的方法，该方法能够完美处理来自极端稀疏雷达孔径的雷达数据。所提出的DNN处理方法既可实现无混叠雷达成像，又能实现超分辨率。通过评估真实仿真数据的检测性能，以及测量点状目标的点扩散函数(PSF)和目标分离性能，验证了实验结果。同时，还针对典型汽车场景进行了定性评估。研究表明，该方法优于最先进的子空间算法及其他现有机器学习解决方案。实验结果证明，使用足够复杂的虚拟输入数据训练的机器学习方法，是雷达信号处理中压缩感知和子空间方法的极具前景的替代方案。该性能的关键在于DNN使用能够完美模仿特定稀疏天线雷达阵列硬件的真实仿真数据作为输入进行训练。以增强虚拟雷达模拟的超高分辨率数据作为真实基准。与其他研究不同，DNN利用了完整的雷达数据立方体，而不仅仅是特定距离-多普勒检测的天线通道信息。经过训练后，所提出的DNN能够实现无旁瓣和无模糊成像，同时提供与全占满阵列几乎相同的分辨率和图像质量。