The University of Texas at Austin
Department of Electrical and Computer Engineering

EE381K-6 Estimation Theory

Fall Semester 2017

Instructor: Prof. Haris Vikalo

Teaching Assistant: Abolfazl Hashemi
Lectures: ECJ 1.314, Tue, Thu 2:00pm-3:30pm

T. Kailath, A. Sayed, and B. Hassibi, Linear Estimation, Prentice Hall, 2000.
S. M. Kay, Fundamentals of Statistical Signal Processing, Vol. I: Estimation Theory, Prentice-Hall, 1993.
B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter, Artech House, 2004.

Additional material: Links and references.


Homework policy: Homeworks are to be submitted at the beginning of the class when they are due. You may discuss homework problems with other students, but must submit your own independent solution. Late homework assignments will not be accepted.

Prerequisites: Background in random processes (EE 381J or equivalent) is required.

Course description: Introduction to the fundamentals of estimation theory, with applications to stochastic and adaptive signal processing. Topics include classical and Bayesian parameter estimation; Cramer-Rao bounds; deterministic and stochastic least-squares estimation; the innovation process; spectral factorization and Wiener filtering; state-space structure and Kalman filters; LMS and RLS adaptive filters; Markov chain Monte Carlo methods; Bayesian and particle filters; expectation-maximization algorithm; non-parametric Bayesian methods.

Course outline (tentative):

Notice for students with disabilities

Students with disabilities may request appropriate academic accommodations from the Division of Diversity and Community Engagement, Services for Students with Disabilities, 471-6259,

Emergency instructions: Classroom evacuation for students

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For evacuation in your classroom or building:
  1. Follow the instructions of faculty and teaching staff.
  2. Exit in an orderly fashion and assemble outside.
  3. Do not re-enter a building unless given instructions by emergency personnel.