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Title: Maximum Likelihood Estimation of Ground Reflectivity from Stripmap SAR Data.

Abstract

Abstract not provided.

Authors:
; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1182995
Report Number(s):
SAND2014-15325J
533998
DOE Contract Number:
AC04-94AL85000
Resource Type:
Journal Article
Resource Relation:
Journal Name: no event information for journal
Country of Publication:
United States
Language:
English

Citation Formats

West, Roger Derek, Gunther, Jacob H., and Moon, Todd K.. Maximum Likelihood Estimation of Ground Reflectivity from Stripmap SAR Data.. United States: N. p., 2014. Web.
West, Roger Derek, Gunther, Jacob H., & Moon, Todd K.. Maximum Likelihood Estimation of Ground Reflectivity from Stripmap SAR Data.. United States.
West, Roger Derek, Gunther, Jacob H., and Moon, Todd K.. Sun . "Maximum Likelihood Estimation of Ground Reflectivity from Stripmap SAR Data.". United States. doi:.
@article{osti_1182995,
title = {Maximum Likelihood Estimation of Ground Reflectivity from Stripmap SAR Data.},
author = {West, Roger Derek and Gunther, Jacob H. and Moon, Todd K.},
abstractNote = {Abstract not provided.},
doi = {},
journal = {no event information for journal},
number = ,
volume = ,
place = {United States},
year = {Sun Jun 01 00:00:00 EDT 2014},
month = {Sun Jun 01 00:00:00 EDT 2014}
}
  • 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 tomore » 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.« less
  • Abstract not provided.
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