ECE565: Estimation, Filtering, and Detection


Course Information Announcements | Course Description | Prerequisites | Textbook |  Topics | Homework  | Grading


Fall 2017

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


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

References:


Topics


Homework & Solutions



Grading Policy

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

Last updated: Sept. 22, 2017