Paper
8 May 1995 Estimation performance at low SNR: predictions of the Barankin bound
Stefan P. Mueller, Marie Foley Kijewski, Christian Kappeler, Stephen C. Moore
Author Affiliations +
Abstract
Nonlinear quantitation tasks can be viewed as parameter estimation problems, and task performance quantified by the variance of parameter estimates. At high signal-to-noise ratio (SNR), the Cramer-Rao bound (CRB), an absolute lower limit on the variance of any unbiased estimator, is a valid predictor of the variance. At low SNR, however, the CRB may not be achievable, i.e., the realizable parameter variances may exceed the CRB. The determination of this SNR threshold, below which efficient estimation is no longer possible, is of great importance for the optimization of quantitative imaging systems. One approach to this problem is calculating the Barankin bound (BB), which at low SNR predicts larger parameter variances than does the CRB. The computation of the BB, which requires selection of a set of test points in parameter space, presents numerical difficulties. Choosing the test points based on (Chi) 2-confidence regions mitigates the numerical problems and renders the BB calculation practical using very high precision calculations in a computer algebra system. Simulations of a nonlinear two-parameter quantitation task demonstrated that the BB can be used to determine the SNR threshold region were ML-estimation performance no longer achieves the CRB. The BB, however, does not converge at low SNR. Therefore, it cannot be used as an absolute standard of achievable performance, and detailed simulations are necessary to investigate optimized strategies for data acquisition and analysis at very low SNR.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stefan P. Mueller, Marie Foley Kijewski, Christian Kappeler, and Stephen C. Moore "Estimation performance at low SNR: predictions of the Barankin bound", Proc. SPIE 2432, Medical Imaging 1995: Physics of Medical Imaging, (8 May 1995); https://doi.org/10.1117/12.208333
Lens.org Logo
CITATIONS
Cited by 9 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Signal to noise ratio

Optical spheres

Computing systems

Monte Carlo methods

Imaging systems

Chemical elements

Data acquisition

Back to Top