Detection of Unknown Signals Under Complex Elliptically Symmetric Distributions

Detection of unknown signals is considered under complex elliptically symmetric (CES) distributions, a wide family that includes several well-known distributions as special cases. We study the detection problem for unknown deterministic signals and random CES signals, both corrupted by CES noise. Fo...

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Bibliographic Details
Published inIEEE transactions on communications Vol. 65; no. 4; pp. 1662 - 1674
Main Authors Baktash, Ebrahim, Karimi, Mahmood, Zhenliang Zhang, Xiaodong Wang
Format Journal Article
LanguageEnglish
Published New York IEEE 01.04.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
CES distributions
Covariance matrices
Detection
Detectors
Economic models
Gaussian distribution
Interference
Light rail systems
Likelihood ratio
non-Gaussian
Performance analysis
Radar
UMP test
unknown signal
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Summary:Detection of unknown signals is considered under complex elliptically symmetric (CES) distributions, a wide family that includes several well-known distributions as special cases. We study the detection problem for unknown deterministic signals and random CES signals, both corrupted by CES noise. For detection of unknown deterministic signals, we form the generalized likelihood ratio test and reduce it to a sufficient test statistic, which is either the norm of the received vector or a function of it. Performance analysis is provided under both known and estimated noise scatter matrix. For detection of random CES signals, we form the likelihood ratio test, provide performance analysis, and establish conditions under which the sufficient test statistic is given by the norm of the received vector. Interestingly, this norm test statistic is proven to be the uniformly most powerful test for the detection of all CES distributed signals.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2017.2662704