ECE565: Estimation, Filtering, and Detection

Fall 2019
School of Electrical Engineering and Computer Science
Oregon State University

Course Information

Instructor:	Dr. Raviv Raich
Office:	Kelley Engineering Center 3009
Email:
Classroom:	KEC 1005
Time:	T Th 14:00-15:50
Office hours:	Th F 4:00-5:00

Announcements

Coursepacks should be available at OSU bookstore in the near future. For pricing, check OSU bookstore.

Course Description

This course will cover two fundamental problems: estimation and detection. For both, Bayesian and non Bayesian estimation will be discussed. Optimal estimation schemes, performance evaluation metrics, and performance bounds such as the Cramer-Rao lower bound will be presented. Filtering will be revisited in the context of linear estimation. Optimal detection schemes for simple hypotheses, uniformly most powerful test, Neyman-Pearson theorem, minimax, likelihood ratio test (LRT) and the generalized LRT will be discussed. Special focus on Gaussian models will be given in both estimation and detection.

Prerequisites

ECE 353 (Intro. to Probability and Random Processes) or equivalent. You are expected to be familiar with concepts such as: random variables, mean, variance, probability density function, or cumulative distribution function.

Textbook

Coursepack: EECS564 Detection, Estimation and Filtering by Prof. Alfred Hero (feedback on the notes would be greatly appreciated)

References:

Fundamentals of Statistical Processing, Volume I: Estimation Theory (Prentice Hall Signal Processing Series) ISBN-10: 013504135X
Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory (Prentice Hall Signal Processing Series) ISBN-10: 0133457117
Detection, Estimation, and Modulation Theory, Part I (Paperback) by Harry L. Van Trees ISBN-10: 0471095176

Topics

Estimation: linear and nonlinear minimum mean squared error estimation, and other strategies.
Linear filtering: Wiener filtering, the orthogonality principle.
Detection: simple, composite, binary and multiple hypotheses. Neyman-Pearson and Bayesian approaches.

Homework & Solutions

Grading Policy

Course project (70%) + HW (30%)

Last updated: Sept. 26, 2019