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## ECED 6530: Random Processes and Estimation Theory

### About Course

Instructor: Dr.

**Christian****Schlegel**, FIEEE, Professor and NSERC ChairProbability 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

**Evaluation**:

- Midterm Exam
- Project Study, report in a paper of length
**no more**then 5 pages (conference style).

Here are the Past Student Projects.