Course Information

Estimation Theory - Fall 2017
Instructor: Prof. Songhwai Oh (오성회)
Email: songhwai (at) snu.ac.kr
Office Hours: Friday 2:00-4:00PM
Office: Building 133 Room 405
Course Number: 430.714
Time: MW 3:30-4:45 PM
Location: Building 301 Room 106
TA: Kyunghoon Cho (조경훈)
Email: kyunghoon.cho (at) cpslab.snu.ac.kr
Office: Building 133 Room 610
 

Course Description

This course introduces classical and modern topics in estimation theory to graduate level students. Topics include minimum variance unbiased estimators, the Cramer-Rao bound, linear models, sufficient statistics, best linear unbiased estimators, maximum likelihood estimators, least squares, exponential family, multivariate Gaussian distribution, Bayes risk, minimum mean square error (MMSE), maximum a posteriori (MAP), linear MMSE, sequential linear MMSE, Bayesian filtering, Kalman filters, extended Kalman filter, unscented Kalman filter, particle filter, data association, multi-target tracking, and Gaussian process regression. Lectures will be in English.

Announcements

Schedule

Week Reading Date Lecture Date Lecture Assignment
1 Kay Ch. 1, 2
Simon Ch. 1, 2
9/4 9/6  
2


Kay Ch. 3.1 - 3.9, Ch. 4

9/11 9/13  
3 Kay Ch. 5, Ch. 6

9/18 9/20

HW1 (due: 10/11)

Kay 3.1, 3.2, 3.4, 3.9, 4.1, 4.5

 

 Kay Ch. 7.1 - 7.6, Ch. 8

    9/22  
4   9/25
  • No class
9/27
  • No class
 
5   10/2
  • Holiday
10/4
  • Holiday
 
6  Kay Ch. 10 10/9
  • Holiday
10/11
  • Exponential family
  • Bayesian approach
 

Textbooks

  • [Recommended] Steven M. Kay, "Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory", Prentice Hall, 1993.
  • [Recommended] Dan Simon, "Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches", Wiley-Interscience, 2006.

Prerequisites

Students must have a solid background in linear algebra, linear system theory, and probability.

Topics

  • Introduction and review of probability and linear system theory
  • Minimum variance unbiased estimators
  • Cramer-Rao lower bound
  • Linear models and sufficient statistics
  • Best linear unbiased estimators and maximum likelihood estimators
  • Least squares, exponential family, and Bayesian approaches
  • Multivariate Gaussian distribution
  • Bayes risk, minimum mean square error (MMSE), and maximum a posteriori (MAP)
  • Linear MMSE and sequential linear MMSE
  • Bayesian filtering
  • Kalman filtering
  • Advanced topics in Kalman filtering
  • Extended Kalman filter, unscented Kalman filter, and particle filter
  • *Data association and multi-target tracking
  • *Gaussian process regression (*if time permits)