DocumentCode :
851647
Title :
Maximum likelihood parameter estimation of superimposed signals by dynamic programming
Author :
Yau, Sze Fong ; Bresler, Yoram
Author_Institution :
Dept. of Electr. & Comput. Eng., Illinois Univ., Urbana, IL, USA
Volume :
41
Issue :
2
fYear :
1993
fDate :
2/1/1993 12:00:00 AM
Firstpage :
804
Lastpage :
820
Abstract :
The problem of fitting a model composed of a number of superimposed signals to noisy data using the maximum likelihood criterion is considered. It is shown, using the Cramer-Rao bound for the estimation accuracy, that in many instances, useful models for the composite signal can be restricted without loss of generality to component signals that directly interact only with one or two of their closest neighbors in parameter space. It is shown that for such models, the global extremum of the criterion can be found efficiently by dynamic programming. The computation requirements are linear in the number of signals, rather than exponential as in the case of exhaustive search. The technique applies for arbitrary sampling of the signals. The dynamic programming method is easily adapted to determining the number of signals as well, as is demonstrated using the minimum description length principle. Computer simulation results are given for several examples
Keywords :
dynamic programming; maximum likelihood estimation; parameter estimation; signal processing; Cramer-Rao bound; MLE; arbitrary sampling; closest neighbors; component signals; composite signal models; computation requirements; dynamic programming; estimation accuracy; global extremum; maximum likelihood criterion; minimum description length; noisy data; number of signals; parameter estimation; parameter space; superimposed signals; Application software; Array signal processing; Computer simulation; Dynamic programming; Geophysics computing; Maximum likelihood estimation; Parameter estimation; Radar applications; Sampling methods; Signal resolution; Signal sampling;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.193219
Filename :
193219
Link To Document :
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