Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data

West, Roger Derek; Gunther, Jacob H.; Moon, Todd K.

doi:10.1109/TAES.2016.140519

Title: Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest 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 »« less

Authors:

West, Roger Derek ^[1]; Gunther, Jacob H. ^[2]; Moon, Todd K. ^[2]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Utah State Univ., Logan, UT (United States)

Publication Date:: Thu Dec 01 00:00:00 EST 2016

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1497661

Alternate Identifier(s):: OSTI ID: 1347353

Report Number(s):: SAND-2016-7475J; SAND-2014-20287J
Journal ID: ISSN 0018-9251; 672262

Grant/Contract Number:: AC04-94AL85000

Resource Type:: 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, Roger 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.  United States: N. p., 2016. 
Web.  doi:10.1109/TAES.2016.140519.

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                    West, Roger 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.  United States.  https://doi.org/10.1109/TAES.2016.140519

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                    West, Roger 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".  United States.  https://doi.org/10.1109/TAES.2016.140519.  https://www.osti.gov/servlets/purl/1497661.

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@article{osti_1497661,

  title        = {Inverse problems-based maximum likelihood estimation of ground reflectivity for selected regions of interest from stripmap SAR data},

  author       = {West, Roger 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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Figure 1: Illustration of the annulus segment of ground that contributes to a single sample taken from the SAR sensor.

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