Duras: Deep unfolded radar sensing using doppler focusing

Pranav Goyal, Satish Mulleti, Anubha Gupta, Yonina C. Eldar

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish
Pages (from-to)4070-4074
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2021-June
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021

Keywords

  • Complex LISTA
  • Deep learning
  • Deep unrolling
  • Partial doppler focusing
  • Radar sensing

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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