Enhanced Imaging for Forward Looking MIMO SAR Via Un-Supervised Deep Basis Pursuit

Vijith Varma Kotte, Shahzad Gishkori, Mudassir Masood, Tareq Y. Al-Naffouri

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations


Nowadays, radar image reconstruction is becoming important in the context of advanced driver assistance systems especially for all weather conditions. In this paper, we present image reconstruction with deep learning based methods on forward looking multiple-input multiple-output array synthetic aperture radar (FL-MIMO SAR). We present deep basis pursuit (DBP) method to solve for convolutional neural network (CNN) weights with unsupervised learning (i.e. without ground-truth) and present modified back projection (MBP) algorithm to reconstruct SAR image with enhanced angular resolution. We present experimental results to verify our proposed methodology on both simulation and real data.

Original languageEnglish (US)
StatePublished - 2022
Event2022 IEEE Radar Conference, RadarConf 2022 - New York City, United States
Duration: Mar 21 2022Mar 25 2022


Conference2022 IEEE Radar Conference, RadarConf 2022
Country/TerritoryUnited States
CityNew York City

Bibliographical note

Publisher Copyright:
© 2022 IEEE.


  • convolutional neural network (CNN)
  • deep basis pursuit (DBP)
  • Forward looking MIMO SAR
  • modified back projection (MBP)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation


Dive into the research topics of 'Enhanced Imaging for Forward Looking MIMO SAR Via Un-Supervised Deep Basis Pursuit'. Together they form a unique fingerprint.

Cite this