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

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

doi:10.1109/RadarConf2248738.2022.9764257

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 conference › Paper › peer-review

2 Scopus citations

Abstract

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 language	English (US)
DOIs	https://doi.org/10.1109/RadarConf2248738.2022.9764257
State	Published - 2022
Event	2022 IEEE Radar Conference, RadarConf 2022 - New York City, United States Duration: Mar 21 2022 → Mar 25 2022

Conference

Conference	2022 IEEE Radar Conference, RadarConf 2022
Country/Territory	United States
City	New York City
Period	03/21/22 → 03/25/22

Keywords

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

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10.1109/RadarConf2248738.2022.9764257

Cite this

@conference{f8d67eb6d5bf4d7facd323fc0ac8b661,

title = "Enhanced Imaging for Forward Looking MIMO SAR Via Un-Supervised Deep Basis Pursuit",

abstract = "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.",

keywords = "convolutional neural network (CNN), deep basis pursuit (DBP), Forward looking MIMO SAR, modified back projection (MBP)",

author = "Kotte, {Vijith Varma} and Shahzad Gishkori and Mudassir Masood and Al-Naffouri, {Tareq Y.}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Radar Conference, RadarConf 2022 ; Conference date: 21-03-2022 Through 25-03-2022",

year = "2022",

doi = "10.1109/RadarConf2248738.2022.9764257",

language = "English (US)",

}

TY - CONF

T1 - Enhanced Imaging for Forward Looking MIMO SAR Via Un-Supervised Deep Basis Pursuit

AU - Kotte, Vijith Varma

AU - Gishkori, Shahzad

AU - Masood, Mudassir

AU - Al-Naffouri, Tareq Y.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - convolutional neural network (CNN)

KW - deep basis pursuit (DBP)

KW - Forward looking MIMO SAR

KW - modified back projection (MBP)

UR - http://www.scopus.com/inward/record.url?scp=85146197725&partnerID=8YFLogxK

U2 - 10.1109/RadarConf2248738.2022.9764257

DO - 10.1109/RadarConf2248738.2022.9764257

M3 - Paper

AN - SCOPUS:85146197725

T2 - 2022 IEEE Radar Conference, RadarConf 2022

Y2 - 21 March 2022 through 25 March 2022

ER -

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

Abstract

Conference

Keywords

ASJC Scopus subject areas

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