Conditionally optimal classification based on CFAR and invariance property for blind receivers

Conditionally optimal classification based on CFAR and invariance property for blind receivers

Abstract This paper proposes a new approach for finding the conditionally optimal solution (the classifier with minimum error probability) for the classification problem where the observations are from the multivariate normal distribution. The optimal Bayes classifier does not exist when the covaria...

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Bibliographic Details
Main Authors: Masoud Naderpour, Hossein Khaleghi Bizaki
Format: Article
Language:English
Published: SpringerOpen 2021-03-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Classification problem
Hypothesis testing problem
Modulation classification
GLR
Separating function estimation test (SFET)
Online Access:https://doi.org/10.1186/s13634-021-00723-9

Internet

https://doi.org/10.1186/s13634-021-00723-9

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