Schedule: 10.09.2018 - 10.12.2018
Contact information for the course (applies in this implementation):
Roland Hostettler (firstname.lastname@example.org)
Office F308, Rakentajanaukio 2
Office hours: Generally between 13.00 and 16.00 on workdays, but requesting an appointment by e-mail beforehand is highly recommended to ensure that I'm available.
Filip Tronarp (email@example.com)
Office F322, Rakentajanaukio 2
Teaching Period (valid 01.08.2018-31.07.2020):
I-II 2018-2019 (autumn)
Learning Outcomes (valid 01.08.2018-31.07.2020):
After successfully completing this course, the participants are able to
- explain the principles and components of sensor fusion systems,
- construct continuous and discrete time state space models based on ordinary differential equations, difference equations, and physical sensor models,
- identify and explain the differences between linear and nonlinear models and their implications on sensor fusion
- develop and compare state space models and Kalman as well as particle filtering algorithms for solving sensor fusion problems.
Content (valid 01.08.2018-31.07.2020):
Assessment Methods and Criteria (valid 01.08.2018-31.07.2020):
Project work, exercises, and final examination
Elaboration of the evaluation criteria and methods, and acquainting students with the evaluation (applies in this implementation):
Achievement of the intended
learning outcomes is assessed through an individual written exam as well
as a group project work. To pass the course, both the written exam and
project work have to be passed. The final grade is the average
of the two.
The grading scale is 0-5.
Workload (valid 01.08.2018-31.07.2020):
36 h contact teaching, 97 h independent studies
Details on calculating the workload (applies in this implementation):
The course consists of 12 lectures and 12 exercise and lab sessions of 1.5 h each, totalling 36 h of contact teaching.
Study Material (valid 01.08.2018-31.07.2020):
Gustafsson: Statistical Sensor Fusion (2012), handouts
Details on the course materials (applies in this implementation):
The course is mainly based on lecture notes and handouts that will be made available on the course homepage. Optionally, the students may also purchase the textbook "Statistical Sensor Fusion" by F. Gustafsson (not mandatory).
Prerequisites (valid 01.08.2018-31.07.2020):
Basics of linear algebra and calculus, basic programming knowledge (MATLAB or Python), basics of statistics.
Grading Scale (valid 01.08.2018-31.07.2020):
Registration for Courses (valid 01.08.2018-31.07.2020):
Further Information (valid 01.08.2018-31.07.2020):
Language class 3: English
Details on the schedule (applies in this implementation):
Lectures: Lectures are held on Mondays, 14:15 - 16:00 in R037/1199 TU6.
- 10.9. - Course Overview and Introduction
- 17.9. - Static Linear Models and Linear Least Squares
- 24.9. - Bayesian Linear Models
- 1.10. - Static Nonlinear Models, Nonlinear Least Squares and Numerical Optimization
- 8.10. - State-Space Models (1)
- 15.10. - State-Space Models (2)
- 22.10. - No lecture (exams)
- 29.10. - State-Space Models (3)
- 5.11. - Kalman Filtering
- 12.11. - Extended Kalman Filtering
- 19.11. - Bootstrap Particle Filtering
- 26.11. - Course Recap
- 3.12. - Project Presentations
- Teacher: Filip Tronarp