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Title: Coherence as an Organizing Principle in Statistical Signal Processing Abstract: In this talk we will begin with a few rudimentary ideas for the representation of signals in the standard Euclidean and Fourier bases, and then talk about filtering in the language of linear algebra.
This will establish a framework for talking about coherence as an organizing principle for signal processing.
EE 571: Statistical Signal Processing, Spring 2001
We will demonstrate that many problems in signal processing lead to coherence. For example, coherence illuminates multiple correlation coefficients and the Cramer-Rao bound. It solves a subspace detection problem, and the problem of detecting dependence between signals measured at co-located or distributed sensors. It leads to a defensible definition of broadband spectral coherence among a multiplicity of time series.