Unsupervised Deep Basis Pursuit Based Resolution Enhancement for Forward Looking MIMO SAR Imaging

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

Research output: Contribution to journalArticlepeer-review


Nowadays, radar based image reconstruction is becoming important in higher-level automated driving, especially for all weather conditions. In this paper, we present unsupervised deep learning method for forward looking multiple-input multipleoutput synthetic aperture radar (FL-MIMO SAR) to enhance the angular resolution. We present mathematical analysis for the composite antenna pattern generated by FL-MIMO SAR as well as image reconstruction with deep learning for FL-MIMO SAR. We present a computationally efficient deep basis pursuit (DBP) method to solve for convolutional neural network (CNN) 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 and compare the performance with compressed sensing based backprojection algorithm on both simulation and real data.
Original languageEnglish (US)
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Aerospace and Electronic Systems
StatePublished - Sep 20 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-09-30
Acknowledged KAUST grant number(s): ORA-CRG2021-4695
Acknowledgements: This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR), under Award No. ORA-CRG2021-4695. The authors would like to thank Jonathan I. Tamir, Assistant Professor of Electrical and Computer Engineering, The University of Texas at Austin, for the useful discussion and explanation on DBP.

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Aerospace Engineering


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