By Topic

On Approximate Diagonalization of Correlation Matrices in Widely Linear Signal Processing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Cheong Took, C. ; Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK ; Douglas, S.C. ; Mandic, D.P.

The so called “augmented” statistics of complex random variables has established that both the covariance and pseudocovariance are necessary to fully describe second order properties of noncircular complex signals. To jointly decorrelate the covariance and pseudocovariance matrix, the recently proposed strong uncorrelating transform (SUT) requires two singular value decompositions (SVDs). In this correspondence, we further illuminate the structure of these matrices and demonstrate that for univariate noncircular data it is sufficient to diagonalize the pseudocovariance matrix-this ensures that the covariance matrix is also approximately diagonal. The proposed approach is shown to result in lower computational complexity and enhanced numerical stability, and to enable elegant new formulations of performance bounds in widely linear signal processing. The analysis is supported by illustrative case studies and simulation examples.

Published in:

Signal Processing, IEEE Transactions on  (Volume:60 ,  Issue: 3 )