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Title: Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data [Regularized maximum likelihood estimation of ground reflectivity from stripmap SAR data]

Abstract

In this study, we derive a comprehensive forward model for the data collected by stripmap synthetic aperture radar (SAR) that is linear in the ground reflectivity parameters. It is also shown that if the noise model is additive, then the forward model fits into the linear statistical model framework, and the ground reflectivity parameters can be estimated by statistical methods. We derive the maximum likelihood (ML) estimates for the ground reflectivity parameters in the case of additive white Gaussian noise. Furthermore, we show that obtaining the ML estimates of the ground reflectivity requires two steps. The first step amounts to a cross-correlation of the data with a model of the data acquisition parameters, and it is shown that this step has essentially the same processing as the so-called convolution back-projection algorithm. The second step is a complete system inversion that is capable of mitigating the sidelobes of the spatially variant impulse responses remaining after the correlation processing. We also state the Cramer-Rao lower bound (CRLB) for the ML ground reflectivity estimates.We show that the CRLB is linked to the SAR system parameters, the flight path of the SAR sensor, and the image reconstruction grid.We demonstrate the ML image formation andmore » the CRLB bound for synthetically generated data.« less

Authors:
 [1];  [2];  [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Utah State Univ., Logan, UT (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1347353
Report Number(s):
SAND-2014-20287J
Journal ID: ISSN 0018-9251; 547566; TRN: US1701190
Grant/Contract Number:
AC04-94AL85000
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Aerospace and Electronics Systems
Additional Journal Information:
Journal Volume: 52; Journal Issue: 6; Journal ID: ISSN 0018-9251
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; maximum likelihood estimation; synthetic aperture radar; inverse problems; image reconstruction; azimuth; time-domain analysis; antennas

Citation Formats

West, R. Derek, Gunther, Jacob H., and Moon, Todd K. Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data [Regularized maximum likelihood estimation of ground reflectivity from stripmap SAR data]. United States: N. p., 2016. Web. doi:10.1109/taes.2016.140519.
West, R. Derek, Gunther, Jacob H., & Moon, Todd K. Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data [Regularized maximum likelihood estimation of ground reflectivity from stripmap SAR data]. United States. doi:10.1109/taes.2016.140519.
West, R. Derek, Gunther, Jacob H., and Moon, Todd K. Thu . "Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data [Regularized maximum likelihood estimation of ground reflectivity from stripmap SAR data]". United States. doi:10.1109/taes.2016.140519. https://www.osti.gov/servlets/purl/1347353.
@article{osti_1347353,
title = {Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data [Regularized maximum likelihood estimation of ground reflectivity from stripmap SAR data]},
author = {West, R. Derek and Gunther, Jacob H. and Moon, Todd K.},
abstractNote = {In this study, we derive a comprehensive forward model for the data collected by stripmap synthetic aperture radar (SAR) that is linear in the ground reflectivity parameters. It is also shown that if the noise model is additive, then the forward model fits into the linear statistical model framework, and the ground reflectivity parameters can be estimated by statistical methods. We derive the maximum likelihood (ML) estimates for the ground reflectivity parameters in the case of additive white Gaussian noise. Furthermore, we show that obtaining the ML estimates of the ground reflectivity requires two steps. The first step amounts to a cross-correlation of the data with a model of the data acquisition parameters, and it is shown that this step has essentially the same processing as the so-called convolution back-projection algorithm. The second step is a complete system inversion that is capable of mitigating the sidelobes of the spatially variant impulse responses remaining after the correlation processing. We also state the Cramer-Rao lower bound (CRLB) for the ML ground reflectivity estimates.We show that the CRLB is linked to the SAR system parameters, the flight path of the SAR sensor, and the image reconstruction grid.We demonstrate the ML image formation and the CRLB bound for synthetically generated data.},
doi = {10.1109/taes.2016.140519},
journal = {IEEE Transactions on Aerospace and Electronics Systems},
number = 6,
volume = 52,
place = {United States},
year = {Thu Dec 01 00:00:00 EST 2016},
month = {Thu Dec 01 00:00:00 EST 2016}
}

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