Achievability of Cramer-Rao Lower Bounds by Multi-Frame Blind Deconvolution Algorithms, Part 2: PSF Estimation

Abstract

Cramer-Rao lower bound (CRB) theory can be used to calculate an algorithm-independent lower bound to the variance of any unbiased estimate of an unknown parameter. The theory also applies to joint estimation of multiple unknown parameters, to functions of estimates, and to estimates that have known bias gradients. CRBs are guaranteed to be lower bounds, but may not be achievable in practice. In particular, it is well known that algorithms that minimize cost functions to generate estimates have difficulty in achieving the CRBs for low signal-to-noise ratios. Our interest is in the achievability of the CRBs by algorithms that use a multi-frame blind deconvolution (MFBD) framework. In previous work, we analyzed the achievability of CRBs for MFBD-based estimation of object energy spectra. Here, we present initial results from our extension of this previous work to the analysis of the achievability of CRBs for MFBD-based estimation of point spread function (PSF) energy spectra that are estimated jointly with the object energy spectra.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2011
Accession Number
ADA550610

Entities

People

  • Charles C. Beckner Jr.
  • Charles L. Matson
  • Michael Flanagan

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Atmospheric Motion
  • Directed Energy Weapons
  • Frequency
  • High Resolution
  • Measurement
  • Military Research
  • Random Variables
  • Space Situational Awareness
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  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Image Processing and Computer Vision.
  • Statistical inference.