Cramer-rao lower bound for parameter estimation of multiexponential signals
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Date
2009-05-18
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
The Cramer Rao Lower Bound on the mean square error of unbiased estimators is widely
used as a measure of accuracy of parameter estimates obtained from a given data. In
this paper, derivation of the Cramer-Rao Bound on real decay rates of multiexponential
signals buried in white Gaussian noise is presented. It is then used to compare the
efficiencies of some of the techniques used in the analysis of such signals. Specifically,
two eigendecomposition-based techniques as well as SVD-ARMA (Singular Value
Decomposition Autoregressive Moving Average) method are tested and evaluated. The
two eigenvector methods were found to outperform SVD-ARMA with minimum norm
being the most reliable at very low SNRs (Signal to Noise Ratios).
Description
Keywords
Parameter estimation, Testing, Integral equations, Mean square error methods, Gaussian noise, Convolution, Deconvolution, Noise generators, Transient analysis, Data engineering
Citation
Jibia, A. U., Salami, M. J. E., Khalifa, O. O., & Elfaki, F. A. (2009, June). Cramer-rao lower bound for parameter estimation of multiexponential signals. In 2009 16th International Conference on Systems, Signals and Image Processing (pp. 1-5). IEEE.