Course Information

Estimation Theory - Fall 2020
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 2:00-3:15 PM
Location: Online (Building 301 Room 103)
TA: Timothy Ha (하디모데)
Email: timothy.ha (at) rllab.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

  • [10/13] The midterm will be held in class on 10/21 (Wed). The exam is closed-book but you can bring one sheet (A4) of hand written notes on a single side (the other side must be blank). You have to turn in this cheat sheet with your exam. Previous midterms: 20182019.
  • [08/28] Please read Ethics of Learning.

Schedule

Week Reading Date Lecture Date Lecture Assignment
1       9/2  
2 Kay Ch. 1
Simon Ch. 1, 2
9/7 9/9  
3


Kay Ch. 2, Ch. 3.1 - 3.9

9/14 9/16

HW 1 (due: 9/23)

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

4 Kay Ch. 4, Ch. 5

9/21 9/23  
5 Kay Ch. 6, Ch. 7.1 - 7.6 9/28 9/30
  • Holiday
 
         

HW 2 (due: 10/7)

Kay 5.3, 5.6, 5.9, 6.1, 6.2, 7.1

6

Kay Ch. 8, Ch. 10

10/5 10/7  
7 Kay Ch. 11 10/12 10/14

HW 3 (due: 10/21)

Kay 8.6, 8.12, 10.10, 10.12, 11.3

8

Kay Ch. 12

10/19 10/21
  • Midterm
  • Time: 2:00-3:15 PM
  • Location: 301-102,103
 
9   10/26 10/28  
10 Simon Ch. 5, Ch. 6 11/2
  • Kalman filter
11/4
  • Alternate Kalman filter formulations
 
11 Simon Ch. 7, Ch. 9 11/9
  • Kalman filter generalizations
11/11
  • Optimal smoothing
 
12 Simon Ch. 9, Ch. 13 11/16
  • Optimal smoothing
11/18
  • Nonlinear Kalman filtering
HW
13 Simon Ch. 14, Ch. 15 11/23
  • Unscented Kalman filter
11/25
  • Particle filtering
 
14   11/30
  • Data association and multi-target tracking
12/2
  • Gaussian process regression
 
15       12/9
  • Final Exam
 

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)