  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.
