The ability to deal with uncertainty in the characteristics of signals is a very important part of advanced signal processing methods. This series of seven lessons introduces you to tools from probability for describing signals that are modeled as having random characteristics. You will learn about auto- and cross-correlation for describing random signals in the time domain, and power spectra, cross spectra, and coherence for describing random signals in the frequency domain. You will also learn how to represent random signals as the output of a linear time-invariant system with white noise input using autoregressive, moving average, and autoregressive moving average models.
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