The University of Texas at Austin
Department of Electrical and Computer
Engineering
EE381K-6 Estimation Theory
Spring Semester 2009
Instructor: Prof. Haris Vikalo
- Email: hvikalo AT ece DOT utexas DOT edu
- Phone: (512) 232-7922
- Office: ACES 3.110
- Hours: Mon, Wed 4:00pm-5:00pm
Teaching Assistant: TBA.
Lectures: BUR 224, Mon, Wed 2:00pm-3:30pm
Textbook: Linear Estimation (T. Kailath, A.Sayed, and
B. Hassibi), Prentice Hall, 2000.
- Additional material: Links and references.
Grading:
- Homeworks: 20%
- Midterm exams: 45%
- Final exam: 35%
Homework policy: There will
be weekly homework assignments. 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),
linear dynamical systems (EE 380K or equivalent), and digital signal processing is
required.
- Course description:
Introduction to the fundamentals of 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; Bayesian filtering; Markov chain
Monte Carlo methods; particle filters; elements of parameter estimation.
- 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
- Bayesian filtering, MCMC methods
- Markov chain Monte Carlo methods
- Particle filters
- Advanced Topics
- Homeworks:
| Problem Set |
Out |
Due |
Problems |
Solutions |
| 1 |
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