On identifiability, maximum-likelihood, and novel HOS based criteria

Considers estimation and classification problems for a stretch of stationary data containing a non-Gaussian linear process and additive Gaussian noise of unknown covariance (AGN/UC). To allow general noncausal and nonminimum phase (NC/NMP) ARMA models, and develop estimation and classification schem...

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Bibliographic Details
Published inFifth ASSP Workshop on Spectrum Estimation and Modeling pp. 217 - 221
Main Author Giannakis, G.B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1990
Subjects
Additive noise
Gaussian noise
Higher order statistics
Image processing
Maximum likelihood estimation
Pattern recognition
Signal processing
Testing
Time domain analysis
Vectors
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Summary:Considers estimation and classification problems for a stretch of stationary data containing a non-Gaussian linear process and additive Gaussian noise of unknown covariance (AGN/UC). To allow general noncausal and nonminimum phase (NC/NMP) ARMA models, and develop estimation and classification schemes which are immune to AGN/UC higher-order statistics (HOS) are resorted to. Time-domain optimality criteria are discussed which employ a finite set of sample cumulant lags, while the frequency-domain criteria involve sample polyspectral lags.< >
DOI:10.1109/SPECT.1990.205578