This video starts to wrap up the Chapter on Stochastic processes by
looking at joint signal statistics, such as cross-correlation and
cross-covariance, uncorrelated pairs of random processes, and an
extension of the various concepts previously developed for analysing
random processes. An example is presented of using cross-covariance as a
surrogate for measuring independence of signals in the classic signal
processing problem of blind source separation. Finally, the Topic
introduces the use of correlation matrices for analysing a finite-block
or window of samples. Correlation matrices are a convenient way of
representing signal statistics when it comes to creating real signal
processing algorithms.
PGEE11164 Probability, Estimation Theory, and Random Signals Lectures -- School of Engineering, University of Edinburgh. Copyright James R. Hopgood and University of Edinburgh, Scotland, United Kingdom (UK). 2020.
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