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
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.
Institute for Digital Communications, Alexander Graham Bell Building, The King's Buildings, Thomas Bayes Road, Edinburgh, EH9 3JL. UK.