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Proceedings Paper

The impact of low signal-to-noise ratio values on the achievability of Cramér-Rao lower bounds with multi-frame blind deconvolution algorithms
Author(s): Charles L. Matson; Michael Flanagan; R. Anthony Vincent
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Paper Abstract

Cramér-Rao lower bound (CRB) theory can be used to calculate algorithm-independent lower bounds to the variances of parameter estimates. It is well known that the CRBs are achievable by algorithms only when the parameters can be estimated with sufficiently-high signal-to-noise ratios (SNRs). Otherwise, the CRBs are still lower bounds, but there can be a large gap between the CRBs and the variances that can be achieved by algorithms. We present results from our initial investigations into the SNR dependence of the achievability of the CRBs by multi-frame blind deconvolution (MFBD) algorithms for high-resolution imaging in the presence of atmospheric turbulence and sensor noise. With the use of sample statistics, we give examples showing that the minimum SNR value for which the CRBs can be achieved by our MFBD algorithm typically ranges between one and five, depending upon the strength of the prior knowledge used in the algorithm and the SNRs in the measured data.

Paper Details

Date Published: 12 October 2010
PDF: 12 pages
Proc. SPIE 7828, Optics in Atmospheric Propagation and Adaptive Systems XIII, 78280M (12 October 2010); doi: 10.1117/12.864330
Show Author Affiliations
Charles L. Matson, Air Force Research Lab. (United States)
Michael Flanagan, SAIC (United States)
R. Anthony Vincent, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 7828:
Optics in Atmospheric Propagation and Adaptive Systems XIII
Karin Stein; John D. Gonglewski, Editor(s)

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