DURAS: Deep Unfolded Radar Sensing Using Doppler Focusing

Sub-Nyquist sampling is used in modern high-resolution pulse-Doppler radar systems to reduce system resources and improve resolution. Xampling with Doppler focusing is utilized to implement these sub-Nyquist radar systems. Signal recovery involves iterative optimization requiring large computational time that may be prohibitive in real applications. In this paper, we propose Deep Unfolded Radar Sensing (DURAS), a model-based deep learning architecture to address this problem. We utilize the recently introduced complex LISTA (C-LISTA) with recurrent neural network units and complex soft-thresholding to handle the complex-valued measurement signals. We propose a partial Doppler focusing (PDF) framework with ensembling of multiple PDF measurement vectors via a convolutional neural network (CNN). This CNN followed by a complex cardioid activation function is added to the front end of the C-LISTA architecture. Thus, DURAS is a hybrid architecture of partial Doppler focusing, CNN, and C-LISTA that provides considerably improved performance compared to existing methods on target detection in radar systems.