Credit Hours: 3-0
Pre-requisites:
1. Stochastic Systems
2. Digital Signal Processing (Basic Concepts)
Course Description:
This is a graduate-level course with applications to communication, control, and signal processing systems. It comprises of decision-theory concepts and optimum-receiver principles, detection of random signals in noise, coherent and noncoherent detection; parameter estimation, linear and nonlinear estimation, and filtering.
Course Contents:
Estimation Part:
General Minimum Variance Unbiased Estimation
Cramer-Rao Lower Bound
Linear Models and Unbiased Estimators
Maximum Likelihood Estimation
Least Squares
Bayesian Estimation
Detection Part:
Statistical Detection Theory
Neyman Pearson Theorem
Matched Receivers
Detection of Deterministic Signals
Detection of Random Signals
Detection of Deterministic Signals with Unknown Parameters
Textbooks:
Fundamentals of Statistical Signal Processing, Vol. I, Estimation Theory, by Steven M. Kay. Prentice Hall, 1998.
Fundamentals of Statistical Signal Processing, Vol. II, Detection Theory, by Steven M. Kay. Prentice Hall, 1998.
Reference books:
Detection, Estimation and Modulation Theory, Part 1: Detection, Estimation and Filtering Theory, 2nd Edition, by Van Trees, 2013.