DocumentCode :
922192
Title :
On optimal linear estimation of signals with general spectral distribution (Corresp.)
Author :
Snyders, Jakov
Volume :
20
Issue :
5
fYear :
1974
fDate :
9/1/1974 12:00:00 AM
Firstpage :
654
Lastpage :
658
Abstract :
It is standard practice to assume that second-order stationary signal and noise processes involved in a linear estimation procedure have spectral densities. A heuristic justification may be based on the reasoning that the part of the signal having singular spectral distribution can be precisely determined, and the part of the noise having singular spectral distribution can be completely eliminated. Rigorous phrasing and proof of this claim are given here for the most general case, i.e., where the singular parts of the spectral distributions may contain continuous components, in which situation the intuitive picture is obscure. The discussion includes both discrete-time and continuous-time processes, and the multivariable case is also treated.
Keywords :
Parameter estimation; Stochastic signals; Additive white noise; Convolution; Councils; Filtering; Fourier transforms; Frequency; Maximum likelihood detection; Nonlinear filters; Signal processing; Transfer functions;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/TIT.1974.1055271
Filename :
1055271
Link To Document :
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