Testing for impropriety of multivariate complex random processes

  title={Testing for impropriety of multivariate complex random processes},
  author={Jitendra K. Tugnait and Sonia A. Bhaskar},
  journal={2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
We consider the problem of testing whether a complex-valued vector random sequence is proper. Past work on this problem is limited to a sequence of independent Gaussian random vectors whereas we allow an arbitrary stationary vector sequence that can be non-Gaussian. A binary hypothesis testing approach is formulated and a generalized likelihood ratio test (GLRT) is derived using the power spectral density estimator of an augmented sequence. An asymptotic analytical solution for calculating the… CONTINUE READING

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Showing 1-10 of 16 references

Statistical Signal Processing of Complex-Valued Data

P. J. Schreier, L. L. Scharf
Cambridge, UK: Cambridge Univ. Press • 2010
View 7 Excerpts
Highly Influenced

An impropriety test based on blockskewcirculant matrices

M. Koller C. Hellings, W. Utschick
Proc . 19 th Intern . ITG Workshop Smart Antennas ( WSA • 2015


C. Hellings
Koller and W. Utschick, “An impropriety test based on block-skew-circulant matrices,” in Proc. 19th Intern. ITG Workshop Smart Antennas • 2015
View 3 Excerpts

A robust estimator and detector of circularity of complex signals

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2011
View 3 Excerpts

RandomMatrix Methods for Wireless Communications

R. Couillet andM. Debbah
View 1 Excerpt

Aspects of multivariate statistical theorywith the application to change detection

2010 IEEE International Geoscience and Remote Sensing Symposium • 2010
View 1 Excerpt

Rubin-Delanchy, “On testing for impropriety of complex-valued Gaussian vectors,

P.A.T. Walden
IEEE Trans. Signal Processing, • 2009
View 3 Excerpts

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