A sequential classification algorithm for autoregressive processes

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2011

Author

Otlu, Güneş

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This study aims to present a sequential method for the classification of the autoregressive processes. Different from the conventional detectors having fixed sample size, the method uses Wald’s sequential probability ratio test and has a variable sample size. It is shown that the suggested method produces the classification decisions much earlier than fixed sample size alternative on the average. The proposed method is extended to the case when processes have unknown variance. The effects of the unknown process variance on the algorithmperformance are examined. Finally, the suggested algorithm is applied to the classification of fixed and rotary wing targets. The average detection time and its relation with signal to noise ratio are examined.

Subject Keywords

Telecommunication.

URI

http://etd.lib.metu.edu.tr/upload/12613722/index.pdf
https://hdl.handle.net/11511/21061

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

G. Otlu, “A sequential classification algorithm for autoregressive processes,” M.S. - Master of Science, Middle East Technical University, 2011.