Abstract/Details

Signal detection and estimation using classification-directed adaptive modeling


1988 1988

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Abstract (summary)

The topic of simultaneous signal detection and classification according to time-varying frequency content is examined. Decision-directed empirical Bayes procedures are used with scalar and vector Markov chain models for modeling the time-varying a priori probability structure. Discrete-time point processes are invoked as a modeling tool for signal presence and detection processes. To take advantage of harmonic signal structure in the nonstationary probability estimates, a new method using classification-directed adaptive modeling for the scalar Markov chain model is developed. Estimation methods employing independent estimation of the marginal probabilities and a distributed network realization of a coupled vector estimation procedure are also developed for comparative purposes. Monte Carlo analysis methods and actual sonar data are used in the evaluation of the various detection/estimation procedures.

Indexing (details)


Subject
Electrical engineering
Classification
0544: Electrical engineering
Identifier / keyword
Applied sciences
Title
Signal detection and estimation using classification-directed adaptive modeling
Author
Muir, Robert Angus
Number of pages
236
Publication year
1988
Degree date
1988
School code
0022
Source
DAI-B 49/04, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
Advisor
Stirling, Wynn C.
University/institution
Brigham Young University
University location
United States -- Utah
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
8809455
ProQuest document ID
303654940
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/303654940
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