Courses Detail Information
ECE6608J – Statistical Signal Processing
Instructors:
Credits: 3 credits
Pre-requisites: VE216, VV214, VE401
Description:
This is a graduate-level introduction to the fundamentals of detection and estimation theory involving signal and system models in which the technical arguments are statistical. The material in this course constitutes a common foundation for work in the statistical signal processing, communication, and control areas. The goal of this course is to get you to develop new ways of thinking about signals and systems and solving problems that involve randomness. You will learn how to model real life problems to include both uncertainties and prior knowledge, and come up with efficient algorithms based on particular optimality criteria.
Course Topics:
Review of probability, statistics, and linear algebra
Suffcient statistics, bias-variance trade-off, minimumvariance unbiased estimators
Linear unbiased estimators, CRB
CRB, minimax estimators
Maximum likelihood estimators
east squares and its variants
Bayes estimators
Wiener filtering
Wiener filtering. Sequential LMMSE
Kalman filtering
Review for parameter estimation
Midterm Exam
Detection problem, NP detector, Bayes detectorBinary hypothesis test and multiple hypothesis testMatched filter
Likelihood ratio test
Generalized likelihood ratio test
Deterministic signal with unknown parametersRandom signal with unknown parametersUnknown noise and colored noise
Review for detection theory
Advanced topics in statistical signal processing