文章基本信息

标题：Conditionally optimal classification based on CFAR and invariance property for blind receivers
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作者：Masoud Naderpour ; Hossein Khaleghi Bizaki
期刊名称：EURASIP Journal on Advances in Signal Processing
印刷版ISSN：1687-6172
电子版ISSN：1687-6180
出版年度：2021
卷号：2021
期号：1
页码：1
DOI：10.1186/s13634-021-00723-9
出版社：Hindawi Publishing Corporation
摘要：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 covariance matrix is unknown for this problem. However, this paper proposes a classifier based on the constant false alarm rate (CFAR) and invariance property. The proposed classifier is optimal conditionally as it has the minimum error probability in a subset of solutions. This approach has an analogy to hypothesis testing problems where uniformly most powerful invariant (UMPI) and uniformly most powerful unbiased (UMPU) detectors are used instead of the non-existing optimal UMP detector. Furthermore, this paper investigates using the proposed classifier for modulation classification as an application in signal processing.
关键词：Classification problem ; Hypothesis testing problem ; Modulation classification ; GLR ; Separating function estimation test (SFET)