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]

Journal Article · Thu Dec 01 00:00:00 EST 2016 · IEEE Transactions on Aerospace and Electronics Systems

DOI:https://doi.org/10.1109/taes.2016.140519· OSTI ID:1347353

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

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

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.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC04-94AL85000
• OSTI ID: 1347353
• Alternate ID(s): OSTI ID: 1497661
• Report Number(s): SAND--2014-20287J; SAND--2016-7475J; 547566
• Journal Information: IEEE Transactions on Aerospace and Electronics Systems, Journal Name: IEEE Transactions on Aerospace and Electronics Systems Journal Issue: 6 Vol. 52; ISSN 0018-9251
• Publisher: IEEE
• Country of Publication: United States
• Language: English

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