DocumentCode :
1151847
Title :
Strongly consistent identification algorithms and noise insensitive MSE criteria
Author :
Delopoulos, Anastasios N. ; Giannaki, Georgios B.
Author_Institution :
Dept. of Electr. Eng., Virginia Univ., Charlottesville, VA, USA
Volume :
40
Issue :
8
fYear :
1992
fDate :
8/1/1992 12:00:00 AM
Firstpage :
1955
Lastpage :
1970
Abstract :
Windowed cumulant projections of nonGaussian linear processes yield autocorrelation estimators which are immune to additive Gaussian noise of unknown covariance. By establishing strong consistency of these estimators, strongly consistent and noise insensitive recursive algorithms are developed for parameter estimation. These computationally attractive schemes are shown to be optimal with respect to a modified mean-square-error (MSE) criterion which implicitly exploits the high signal-to-noise ratio domain of cumulant statistics. The novel MSE objective function is expressed in terms of the noisy process, but it is shown to be a scalar multiple of the standard MSE criterion as if the latter was computed in the absence of noise. Simulations illustrate the performance of the proposed algorithms and compare them with the conventional algorithms
Keywords :
correlation methods; error statistics; parameter estimation; signal processing; additive Gaussian noise; autocorrelation estimators; cumulant statistics; high signal-to-noise ratio domain; modified mean-square-error; noise insensitive MSE criteria; nonGaussian linear processes; parameter estimation; recursive algorithms; scalar multiple; signal processing; windowed cumulant projections; Additive noise; Autocorrelation; Colored noise; Finite impulse response filter; Gaussian noise; Parameter estimation; Recursive estimation; Signal to noise ratio; Statistics; Yield estimation;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.149997
Filename :
149997
Link To Document :
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