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ECED 6530: Random Processes and Estimation Theory

About Course

Instructor: Dr. Christian Schlegel, FIEEE, Professor and NSERC Chair
Probability theory: mathematical model, conditional probabilities, random variables, cdf and pdf, transformation of random variables, conditional densities, statistical averages. Random processes concept; ensemble, stationarity, ergodicity, correlation and covariance, power spectral density, calculation and measurement of ACF, AVF and PSD, Gaussian random processes, noise. Transmission of random processes through linear systems: time-invariant systems, non-stationary processes, random walks.
Estimation theory: basics, best estimators, conditional expectation, parametric estimation theory, Fisher information, Cramer-Rao lower bound, Bayesian estimation, minimum mean-square error estimation (MMSE), recursive MMSE formulations, Kalman filtering concepts, the extended Kalman filter and applications, the unscented transform, unscented Kalman filter, particle filtering.
Prerequisites: Background information, Pages 1-12 of course notes
  • Midterm Exam
  • Project Study, report in a paper of length no more then 5 pages (conference style). 

Here are the Past Student Projects.

Course Materials

Course Assignments