Course syllabus adopted 2021-02-26 by Head of Programme (or corresponding).
Overview
- Swedish nameSensorfusion och olinjär filtrering
- CodeSSY345
- Credits7.5 Credits
- OwnerMPSYS
- Education cycleSecond-cycle
- Main field of studyAutomation and Mechatronics Engineering, Computer Science and Engineering, Electrical Engineering
- DepartmentELECTRICAL ENGINEERING
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 35119
- Maximum participants100 (at least 10% of the seats are reserved for exchange students)
- Block schedule
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0117 Written and oral assignments 7.5 c Grading: TH | 7.5 c |
In programmes
- MPICT - INFORMATION AND COMMUNICATION TECHNOLOGY, MSC PROGR, Year 1 (compulsory elective)
- MPMED - BIOMEDICAL ENGINEERING, MSC PROGR, Year 1 (compulsory elective)
- MPSYS - SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (compulsory elective)
Examiner
Lars Hammarstrand- Associate Professor, Signal Processing and Biomedical Engineering, Electrical Engineering
Eligibility
General entry requirements for Master's level (second cycle)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Course specific prerequisites
Working knowledge of basic probability, statistics, and linear algebra. Basic MATLAB programming skills are also required in order to complete the home assignments and the course projects.
Aim
The purpose with this course is to give a thorough introduction to sensor fusion in time-varying settings (also known as filtering or smoothing), i.e., how to perform state estimation using a variety of sensors. Such methods continue to receive considerable attention due to their high versatility; famous examples include that they enabled the landing on the moon and that they are currently important for the development of self-driving cars.
In the course we emphasize on positioning of vehicles, people, mobile phones, robots, etc, though the potential applications go way beyond that. The intention with the course is to provide a solid theoretical background and to give hands-on experience on how to apply the techniques to solve problems of practical importance.
Learning outcomes (after completion of the course the student should be able to)
After the course, students should be able to
- explain the fundamental principles in Bayesian estimation
- describe and model commonly used sensors' measurements
- summarize and compare the most typical motion models in positioning in order to know when to use them in practical problems
- derive the expression for an optimal filter
- describe the essential properties of the Kalman filter (KF) and apply it on linear state space models
- implement the key nonlinear filters (above all the extended Kalman filter, unscented Kalman filter and the particle filter) in Matlab, in order to solve problems with nonlinear motion and/or sensor models
- select a suitable filter method by analyzing the properties and requirements in an application
- apply the linear and nonlinear Rauch-Tung-Striebel smoothers to general smoothing problems
- solve a variety of important real-world filtering and smoothing problems, by employing and adapting the above knowledge to a variety of applications.
Content
The overall problem is to use data from several different types of sensors to estimate an unknown state (here often containing position, velocity, etc). We cover the main concepts, models and methods:
- Bayes rule and Bayesian estimation (e.g., MMSE estimators)
- Optimal filtering and optimal smoothing
- Models of discrete time systems with uncertainty: both both motion models (e.g., the constant velocity model) and sensor models.
- Sigma point methods
- Linear and nonlinear Kalman filters (KF, EKF, UKF, CKF, etc)
- Particle filters
- Linear and nonlinear Rauch-Tung-Striebel smoothers
- Application of all of the above on various problems
Organisation
The course comprises on-line lectures (to watch before the class), practice sessions (where we review material from the corresponding lecture), home assignments, projects, tutorial sessions (related to the home assignments and projects) and two oral exams.
Literature
We mainly useSimo Särkkä, Bayesian Filtering and Smoothing. Cambridge University Press, 2013.
which is available online http://becs.aalto.fi/~ssarkka/pub/cup_book_online_20131111.pdf
Examination including compulsory elements
There is no written exam in this course. Instead the students are evaluated individually based on their performance in the different activities in the course; more specifically, the grade is obtained by weighting the results on hand-ins, projects and oral examinations and the degree of attendance.
The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.