Title :
Noise Parameter Estimation From Quantized Data
Author :
Moschitta, Antonio ; Carbone, Paolo
Author_Institution :
Dept. of Electron. & Inf. Eng, Perugia Univ.
fDate :
6/1/2007 12:00:00 AM
Abstract :
In this paper, the parametric estimation of the variance of white Gaussian noise is considered when available data are obtained from a quantized noisy stimulus. The Crameacuter-Rao lower bound is derived, and the statistical efficiency of a maximum-likelihood parametric estimator is discussed, along with the estimation algorithm proposed in IEEE Standard 1241
Keywords :
Gaussian noise; maximum likelihood estimation; parameter estimation; quantisation (signal); statistical analysis; white noise; Cramer-Rao lower bound; IEEE Standard 1241; analog-to-digital conversion; estimation algorithm; maximum-likelihood parametric estimator; noise parameter estimation; noise parametric estimation; quantized data; quantized noisy stimulus; statistical efficiency; white Gaussian noise; AWGN; Additive white noise; Algorithm design and analysis; Costs; Gaussian noise; Manufacturing processes; Maximum likelihood estimation; Parameter estimation; Production; Testing; Analog-to-digital conversion (ADC); Cramér–Rao lower bound (CRLB); maximum likelihood; noise parametric estimation; statistical efficiency;
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on
DOI :
10.1109/TIM.2007.895575
