
^{4 credit points}
_{Start: February 2011}
_{End:}_{ August}_{ 2011}
_{Frequency: Annually, Spring semester}
Angela Schoellig, Sebastian Trimpe
Wednesdays
13:1516:00, CHN C 14
Description: Introduction to estimation; probability review; Bayes theorem; Bayesian tracking; standard Kalman filter; extended Kalman filter; particle filtering; observers and the separation principle.
Literature: Class notes (will be available online).
Requirements: Introductory probability theory and matrixvector algebra.
Sep 19 
The final grades for the class have been forwarded to the departments and should be available through mystudies soon. If you want to take a look at your graded exam or if you have any questions regarding your final grade, please send an email to the teaching assistants. The sample solution of the final exam may be downloaded here. 
Sep 08 
You can find the results for Programming Exercise 2 (Particle Filtering) in the section below. There will be no sample solution of the programming exercise available online. If you have questions regarding your programming exercise or would like to see our solution, please make an appointment with Angela. 
Aug 11 
Please note that we added an Errata document to the Quizzes and exams of past years section. Thanks everyone who is pointing out typos and mistakes. 
Jul 25 
The lecture notes of the last class on Observers and the Separation Principle are online. 
Jul 22 
The complete version of Problem Set 6 has been uploaded; a solution to problem 4 (counterexample separation principle) has been added (no other changes). If you found other counterexamples to the separation principle, we'd be very interested to know your solution. 
Jul 22 
Angela and Sebastian will offer office hours in the week of and before the exam (see below under "class facts" for details). 
Jul 12 
You can find the results of the course evaluation at the DMAVT website (ETH login required). Thanks to all of you who participated in the evaluation. Your comments are very helpful to us for improving the class in future years! 
Jun 08 
You can find the results for Programming Exercise 1 (Kalman Filtering) in the section below. There will be no sample solution of the programming exercise available online. If you have questions regarding your programming exercise or would like to see our solution, please make an appointment with Sebastian. 
May 31 
Problem Set 6 is now online (except for a solution to problem #4, which will be posted later). 
May 24 
There will be no lecture notes or problem set available for download this week. The reason is that the material on "Observers and the separation principle" will be taught in this class for the first time. It will therefore take us some time to prepare the material; it will however be available well in advance before the final exam. 
May 18 
Angela will have office hours for programming exercise 2 on May 20 and June 6 (time and place, see below under "class facts"). Please come to one of these if you have questions regarding the programming exercise. 
Apr 26 
If you want to take a look at your graded quiz, we offer additional office hours on May 04 from 12:00 to 13:00 in ML K33. 
Apr 21 
The quiz results and sample solutions are available (see section Quizzes and Programming Exercises below). 
Apr 20 
Sebastian will have office hours for programming exercise 1 on May 4 and May 9 (time and place, see below under "class facts"). Please come to one of these if you have questions regarding the programming exercise. Notice that there is no lecture/exercise class on May 4. 
Apr 05 
IMPORTANT: We had to change the room for the quiz. New location is CHN C 14 (our regular lecture room). 
Mar 30 
Information regarding the Quiz:

Mar 15 
There are lists of correction to the lecture notes and the problem sets available under "Errata" in the corresponding sections. We will update these lists during the semester, so please check regularly.
Thanks to all of you who have been reporting typos to us. This is very helpful for us; please continue reporting mistakes that you find. 
Mar 9 
Additional material presented during the exercise classes is found under "Exercise Classes" (below). The summary slide of the first exercise class was added. 
Mar 2 
Please note that there is a mistake in lecture notes #1, on page 1, under "Resulting algorithms": instead of "Gaussian noise" in item 2 and 3, it should read "Gaussian distributions"; and instead of "NonGaussian noise" in item 4, it should read "NonGaussian distributions."
Generally, we will post mistakes that we found in the lecture notes here (under "Announcements"). Please check regularly. 
Mar 1 
This sheet summarizing class facts will be distributed in the first lecture. 
Feb 15 
Please note that in the first week of the semester, there will be no lecture and no exercise class. The first lecture of the Recursive Estimation class will be on Mar 02. 
Jan 14 
We got a bigger room for our course. Please note that the class is taking place in CHN C 14. 
Jan 11 
This website has been updated with class syllabus and information regarding quiz, programming exercises and problem sets. 
Jan 4 
The class Recursive Estimation will be taught by Prof. Raffaello D'Andrea in Spring 2011. The class has been renamed (Spring 2010: Introduction to Recursive Filtering and Estimation), but the class content will essentially remain the same.
More information on the class will follow here soon. 
Instructor  Prof. Raffaello D'Andrea 
Teaching Assistants 
Angela Schoellig, Sebastian Trimpe 
Lectures 
Wednesday, 13:15 to 15:00, CHN C 14 
Exercise class 
Wednesday, 15:15 to 16:00, CHN C 14 
Office hours 
By appointment (please send an email to the teaching assistants).
Office hours for programming exercise 1 (Sebastian):
Office hours for programming exercise 2 (Angela):
Office hours before exam (Angela and Sebastian):

Exam  Final written exam during the examination session, covers all material. 
Grading 
40% quiz/programming exercises, 60% final exam if the grade for quiz and programming exercises is better than the grade in the final exam; 100% final exam otherwise. 
Only the two best grades from the quiz and the programming exercises will count towards the 40% above. 

PhD students will get credits for the class if they pass the class (final grade of 4.0 or higher).  
Repetition  The final exam is only offered in the session after the course unit. Repetition is only possible after reenrolling. Students who took the class in previous semesters and have to retake the course should inform the teaching assistants before the beginning of the new class. 
#  date  topic  reading 
1 
Mar 02 
Introduction to Estimation and Random Variables 
Lecture Notes 1 
2 
Mar 09 
Probability Review 
Lecture Notes 2 
3 
Mar 16 
Bayes Theorem 
Lecture Notes 3a Lecture Notes 3b 
4 
Mar 23 
Bayesian Tracking 
Lecture Notes 4 
5 
Mar 30 
Extracting Estimates from Probability Distributions 
Lecture Notes 5 
6 
Apr 06 
Kalman Filtering: Preliminaries 
Lecture Notes 6 
7 
Apr 13 
Kalman Filtering: Algorithm 
Lecture Notes 7 
8 
Apr 20 
Extended Kalman Filtering 
Lecture Notes 8 
Apr 27 
Easter break (no class) 

May 04 
no class 

9 
May 11 
Particle Filtering: Preliminaries 
Lecture Notes 9 
10 
May 18 
Particle Filtering: Algorithm 
Lecture Notes 10 
11 
May 25 
Observers and the Separation Principle 
Lecture Notes 11 
Jun 01 
no class 
Lecture notes for each topic will be made available online approximately one week before the topic is covered in the lecture.
Here is a list of corrections to the lecture notes. It will be updated regularly during the semester.
Please report typos that you may find in the notes to the TAs.
#  date  topic 
additional material 
1 
Mar 09 
Probability Review 
Summary Slide 1 
2 
Mar 16 
Sampling a Distribution 
Summary Slide 2 Matlab code 
3 
Mar 23 
Bayes Theorem and Bayesian Tracking 
Summary Slide 3 Example 2 
4 
Mar 30 
Extracting Estimates from PDFs 
Summary Slide 4 
5 
Apr 13  Kalman Filter  Example on Observability 
Summary Slide 5 Matlab code 
6 
Apr 20 
Kalman Filter  Inverted Pendulum Example 
Summary Slide 6 
7 
May 11  Extended Kalman Filter  Inverted Pendulum Example 
Summary Slide 7 Matlab code 
8 
May 18 
Particle Filter (Part 1) 
Summary Slide 8 
9 
May 25 
Particle Filter (Part 2) 
Summary Slide same as #8 
During the semester, there will be a graded quiz and programming exercises, which can be used to improve the final grade for the course (see "grading"). The quiz will take place at the beginning of the lecture and will test the student's understanding of the corresponding topic.
The programming exercises will require the student to apply the lecture material. Up to three students can work together on the programming exercises. If they do, they have to hand in one solution per group and will all receive the same grade.
#  type  topic  dates  download 
Q1 
Quiz 
Probability, Bayes Theorem, Estimation (Lectures #1 to #5) 
Apr 06 
Results Solution 
P1 
Programming 
Kalman Filtering 
Apr 20 (issued), May 11 (due) 
Exercise Matlab template 
P2 
Programming 
Particle Filtering 
May 18 (issued), Jun 08 (due) 
Exercise Matlab template 
We will make sets of problems and solutions available online for the topics covered in the lecture. It is the student's responsibility to solve the problems and understand their solutions. The TAs will answer questions in office hours and some of the problems might be covered during the exercise classes.
# 
topic 
download 
1 
Probability Review 
Problem Set 1 
2 
Bayes Theorem and Bayes Tracking 
Problem Set 2 
3  Extracting Estimates from Probability Distributions 
Problem Set 3 
4  Kalman Filtering  Problem Set 4 
5 
Particle Filtering 
Problem Set 5 
6 
Observers and the Separation Principle 
Problem Set 6
Matlab code (Problem 2a, Problem 2b) 
Here is a list of corrections to the problem sets. It will be updated regularly during the semester.
Please report typos that you may find in the notes to the TAs.
You can find quiz and exam of the past year including their sample solutions below.
2010 Quiz (Topic: Probability, Bayes Theorem, Estimation) 
2010 Final Exam 
Here is a list of corrections to the exam and quiz. It will be updated regularly during the semester.
Please report typos that you may find in the notes to the TAs.
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