
^{4 credit points}
_{Start: 03.03.10}
_{End: }_{02.06.10}
_{Frequency: Annually, Spring semester}
Angela Schöllig, Sebastian Trimpe
Wednesdays
13:1516:00, NO C 6
(on April 14 only: HG F 5)
Description: Probability review; Bayes theorem; recursive estimation using Bayes theorem; introduction to estimation; standard Kalman filter; extended Kalman filter; particle filtering.
Literature: Notes (available online): Introduction to Estimation and the Kalman Filter by H. DurrantWhyte and other notes.
Requirements: Introductory probability theory and matrixvector algebra.
Sep 10 
The sample solution of the final exam may be downloaded here. 
Sep 07 
The final marks (percentages) for the class may be found here. For Master students, the final grades will be published by the ETH student administrations. Please contact the teaching assistants if you have any question regarding the grading. If you want to take a look at your graded exam, please come to ML K33 on Sep 16 from 14:00 to 15:00. 
July 30 
The results of the second programming exercise are online. If you have questions or if you want to take a look at the sample solution (will not be available online), please make an appointment with Angela. 
July 19 
There will be office hours held by Angela and Sebastian during the semester break on the following dates:
If you have questions regarding the preparation for the exam, please come to one of these dates. 
June 30 
Final Examination: According to the information by the ETH examination office, the final exam will take place on August 23, 2010 from 14:0016:30 in ETA F5. We will offer office hours before the final examination; they will be announced here. 
June 03 
Example solution of Programming Exercise 2: A complete package including the Matlab Compiler and a Readme File was uploaded. 
May 30 
The results of the first programming exercise are online. If you have questions or if you want to take a look at the sample solution (will not be available online), please make an appointment with Sebastian. 
May 28 
Programming Exercise 2: An example solution was uploaded, see 'Quizzes and Programming Exercises' section. You can use it to compare your code with. 
May 27 
Programming Exercise 2: An updated template was uploaded. The halfplane measurement was incorrect before. 
May 25 
In class, the variance update equation for the Kalman Filter (2nd step) was derived as follows: P(kk) = (I  KH) P(kk1) (I  KH)^T + KRK^T. Some students asked why we weren't using the shorter formula P(kk) = (I  KH) P(kk1) which can be derived from the first by using the equation of the filter gain K. The reason why the first equation for P(kk) is preferred over the second is that it is symmetric and therefore preserves the symmetry of P(kk) in the case of numerical errors. 
May 17 
A link to the class notes for the two lectures on particle filtering has been added in the Lectures section below. The Matlab script that will be used in this week's lecture (May 19) to generate approximations of a Gaussian distribution can be downloaded here. 
Apr 30 
An updated version of the programming exercise has been uploaded (Ver 2). We have added in the problem description that all random variables are assumed to be mutually independent and independent over time. (Thanks for pointing this out.) 
Apr 28 
The first programming exercise is online. It is due on May 12. 
Apr 19 
The quiz results and sample solutions are available (see section Quizzes and Programming Exercises below). 
Mar 31 
Information regarding the Quiz:

Mar 26 
Please notice an update on the class schedule: the lecture #7 will be given by Prof. D'Andrea on Apr 15 (Thu) from 18:15 to 20:00; the same lecture will be repeated by Sebastian on Apr 21 (regular lecture time). The date for the exercise class remains unchanged (Apr 21, 15:15 to 16:00). 
Mar 24 
The .m file for Problem 12 (Problem Set 2) is added and a slightly updated version of Problem Set 2 is uploaded. 
Mar 17 
Additional material for today's exercise class can be downloaded here (two different implementations of the random number generator for the joint pdf example, script for plotting the results, and the derivation of the second implementation that was not covered in class). 
Mar 12 
Due to unavailability of bigger rooms, the classroom for the lecture and the exercise remains the same (NO C 6). There is one exception: On April 14 (day of the quiz), the class will take place in HG F 5. 
Mar 03 
The factsheet that was handed out in class today can be downloaded here. 
Feb 10 
The first class will take place on March 3. 
Feb 10 
This website has been updated with the syllabus and other class information. 
Jan 27 
The class Introduction to Recursive Filtering and Estimation will be held by Prof. Raffaello D'Andrea in Spring 2010. The syllabus is currently under revision; more details will be announced here soon. 
Instructor  Prof. Raffaello D'Andrea 
Teaching Assistants 
Angela Schoellig, Sebastian Trimpe 
Lectures 
Wednesday, 13:15 to 15:00, NO C 6, (on April 14 only: HG F 5) 
Exercise class  Wednesday, 15:15 to 16:00, NO C 6, (on April 14 only: HG F 5) 
Office hours 
During the break: Aug 11/16/20, 14:0015:00, ML K33. During the semester: By appointment (please send an email to the teaching assistants). 
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 Spring 09 and have to retake the course should inform the teaching assistants before the beginning of the new class. 
#  date  topic  reading 
1 
Mar 03 
Probability Review 
DW: 1, 2 
2 
Mar 10 
Probability Review 
DW: 1, 2 
3 
Mar 17 
Bayes Theorem 
DW: 1, 2 
4 
Mar 24 
Bayesian Tracking 
DW: 1, 2 
5 
Mar 31 
Introduction to Estimation 
DW: 3 
 
Apr 7 
Easter break 
 
6 
Apr 14 
Standard Kalman Filter 
DW: 4, 5, 6 
7 (*) 
Apr 15 
Standard Kalman Filter 
DW: 4, 5, 6 
7R (*) 
Apr 21 
" 
" 
8 
Apr 28 
Extended Kalman Filter 
DW: 7 
 
May 05 
No class 
 
 
May 12 
No class 
 
9 
May 19 
Particle Filtering 
PF Tutorial 
10 
May 26 
Particle Filtering 
PF Tutorial 
 
Jun 02 
No class 
 
Remarks:
(*) The lecture will be held by Prof. D'Andrea on April 15 (Thu) from 18:15 to 20:00 in NO C6. The same lecture will be repeated by one of the teaching assistants on April 21 at the regular place/time.
The notes Introduction to Estimation and Kalman Filter by DurrantWhyte are the primary class reference. The sections relevant to the lectures are indicated above.
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 14 
Results 
P1  Programming 
Kalman filter 
Apr 28 (issued)
May 12 (due) 
Exercise MatlabTemplate 
P2  Programming 
Particle filter 
May 26 (issued)
Jun 09 (due) 
Exercise MatlabTemplate ExampleSolution (.exe, complete package with compiler) 
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 
ProblemSet1 
2 
Bayes theorem, recursive estimation using Bayes theorem 
ProblemSet2 
3  Introduction to estimation  ProblemSet3 
4  Kalman filter 
ProblemSet4 
5 
Particle filter 
ProblemSet5 
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