Upon successful completion of this course, students will have achieved an understanding of the basic methodologies of analysis and stochastic signal processing principles through probabilities and distributions.
Course Content (Syllabus)
Probability elements. Random variables. Moment models. Distributions. Continuous and Discrete Stochastic processes. Ergodicity and stationarity of stochastic processes. Input-output relations of linear system with stochastic input. Power spectra. Autocorrelation and crosscorrelation function. Noise modelling. Optimal linear systems theory. Deterministic and stochastic signals with noise. Wiener-Kolmogorov theory for continues signals. Estimation with casual and non-casual filters.
stochastic signal, autocorrelation, power spectrum, probability
Additional bibliography for study
-Mix, Dwight F. Random signal processing. Englewood Cliffs: Prentice Hall, 1995.
-Yates, Roy D., and David J. Goodman. Probability and stochastic processes. USA: John Wiley & Sons, 1999.