Multiantenna Assisted Source Detection in Toeplitz Noise Covariance

This letter addresses the problem of signal detection in additive correlated noise whose covariance matrix is Toeplitz. Particularly, we design a novel detection approach in the framework of generalized likelihood ratio test, in which the maximum likelihood (ML) estimate of the Toeplitz covariance m...

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Bibliographic Details
Published in:IEEE signal processing letters 2019-06, Vol.26 (6), p.813-817
Main Authors: Yu-Hang Xiao, Junhao Xie, Lei Huang, So, H. C.
Format: Article
Language:English
Subjects:
Asymptotic properties
Closed-form solutions
Constant false alarm rate
Correlation
Covariance matrices
Covariance matrix
Detectors
Economic models
Estimating techniques
generalized likelihood ratio test
Iterative algorithms
Iterative methods
Likelihood ratio
Maximum likelihood estimates
Maximum likelihood estimation
Signal detection
Source detection
Testing
Toeplitz noise covariance
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Summary:This letter addresses the problem of signal detection in additive correlated noise whose covariance matrix is Toeplitz. Particularly, we design a novel detection approach in the framework of generalized likelihood ratio test, in which the maximum likelihood (ML) estimate of the Toeplitz covariance matrix is needed. Since there are no closed-form expressions for this ML estimate, we resort to the inverse iterative algorithm. The proposed detector surpasses existing methods in detection power and enjoys the constant false-alarm rate property. Besides, accurate asymptotic null and non-null distributions of the test statistic are derived. Numerical results are presented to validate our theoretical findings.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2019.2905370