There are two statistical signal processing courses in the graduate program of the Department of Electrical and Electronic Engineering. "EE 503 Statistical Signal Processing and Modelling" is offered in Fall semesters. "EE 5506 Advanced Statistical Signal Processing" is offered in Spring semesters.
EE 503 Course Content:
Random processes. Power spectral density. Auto-regressive processes. Moving-average processes. Periodic processes. Spectral decomposition. Whitening filter. Innovations. Stochastic signal models. Yule-Walker equations. Linear-time invariant filtering of random processes. Estimation. Linear Estimators. Linear minimum mean square error estimator. Wiener filter. Optimal FIR filters. Optimal IIR filters. Filtering, prediction, smoothing applications. Reduced dimension stochastic signal representation. Karhunen-Loeve transform.
Prerequisite: Probability Theory, Signals and Systems, Digital Signal Processing.
EE 5506 Course Content:
Textbook: Kay, Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory, 1993.
Introduction to Estimation
Minimum Variance Unbiased Estimation
Cramer-Rao Bound
Linear Models
Best Linear Unbiased Estimator
Maximum Likelihood Estimation
Bayesian Philosophy
General Bayesian
Linear Bayesian (Wiener Filter)
Kalman Filter
Sequential Monte Carlo Methods
Other topics
Prerequisite: Advanced Probability Theory, EE 503 is strongly recommended.