EE 381K-6 Estimation Theory
Course Information
Instructor: Haris Vikalo
- Email: hvikalo AT ece DOT utexas DOT edu
- Phone: TBA
- Office: TBA
- Hours: TBA
Teaching Assistant : TBA
Lectures:
- Time: TTh 3:30-5:00 PM
- Place: CPE 2.220
Textbook: Linear Estimation (T. Kailath, A.Sayed, and
B. Hassibi), Prentice Hall, 2000.
Grading (tentative):
- Homeworks: 20%
- Midterm exams: 35%
- Final exam: 45%
Homework policy: You are allowed, even encouraged, to discuss
homework questions, but please be sure to submit your own independent
solution. Late homework assignments will not be accepted.
- Prerequisites:
Background in random processes (EE 381J or equivalent), linear dynamical
systems (EE 380K or equivalent), and digital signal processing is required.
- Course description:
Introduction to the fundamentals of linear estimation theory, with
applications to stochastic and adaptive signal processing. Topics include
deterministic and stochastic least-squares estimation; the innovation process;
spectral factorization and Wiener filtering; state-space structure and Kalman
filters; array and fast array algorithms; displacement structure and fast
algorithms; LMS and RLS adaptive filters.
- Course outline (tentative):
- Introduction and Foundations
- Overview
- Deterministic least-squares problems
- Stochastic least-squares problems
- The innovation process
- State-space models
- Estimation of Stationary Processes
- Innovations for stationary processes
- Wiener theory for scalar processes
- Recursive Wiener filters
- Estimation of Nonstationary Processes
- The Kalman filter
- Smoothed estimators
- Fast and Array Algorithms
- Fast algorithms
- Array algorithms
- Fast array algorithms
- Continuous-Time Estimation
- Continuous-time state-space estimation
- Advanced Topics