| Paper ID | MLSP-47.3 |
| Paper Title |
DURAS: Deep Unfolded Radar Sensing Using Doppler Focusing |
| Authors |
Pranav Goyal, Indraprastha Institute of Information Technology Delhi, India; Satish Mulleti, Weizmann Institute of Science, Israel; Anubha Gupta, Indraprastha Institute of Information Technology Delhi, India; Yonina C. Eldar, Weizmann Institute of Science, Israel |
| Session | MLSP-47: Applications of Machine Learning |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [SMDSP-SAP] Sparsity-aware processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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. |
