Statistical signal processing of complex-valued data

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• Dr Peter Schreier, University of Newcastle, Australia
• Friday 21 August 2009, 14:00-15:00
• Gabor Seminar Room, 611.

If you have a question about this talk, please contact Danilo Mandic.

Complex-valued random signals are embedded into the very fabric of science and engineering, essential to communications, radar, sonar, geophysics, oceanography, optics, acoustics, and other areas. This talk illuminates why complex-valued problem descriptions are often more meaningful and powerful than real bivariate descriptions.

In the past, it was often assumed that complex random signals are proper and circular. A proper complex random variable is uncorrelated with its complex conjugate, and a circular complex random variable has a probability distribution that is invariant under rotation in the complex plane. These assumptions are convenient because they simplify computations and make complex signals behave very much like real-valued signals. Yet in many cases proper and circular random signals are very poor models of the underlying physics. This talk presents some tools and algorithms that are necessary to deal with improper and noncircular signals, and shows that this can have significant payoffs.

Biosketch: Peter Schreier is currently an Associate Professor in the School of Electrical Engineering and Computer Science at the University of Newcastle, New South Wales, Australia. He received a Master of Science from the University of Notre Dame, Indiana, USA , in 1999, and a Ph.D. from the University of Colorado at Boulder, USA , in 2003, both in electrical engineering. He has held visiting appointments at the University of Hawaii, USA , Friedrich-Alexander-Universität Erlangen, Germany, and Colorado State University, USA . He currently serves on the Editorial Board of the IEEE Transactions on Signal Processing, and on the IEEE Technical Committee Machine Learning for Signal Processing.

This talk is part of the COMMSP Seminar series.

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