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

1 Scopus citations

Abstract

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
DOIs
StatePublished - Sep 20 2023

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Aerospace Engineering

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