Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
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.
Schedule: 08.09.2020 - 08.12.2020
Teacher in charge (valid 01.08.2020-31.07.2022): Lauri Palva, Simo Särkkä
Teacher in charge (applies in this implementation): Lauri Palva, Simo Särkkä
Contact information for the course (applies in this implementation):
Main lecturer Prof. Simo Särkkä (email@example.com)
Secondary lecturers Dr. Muhammad Emzir (firstname.lastname@example.org) and Dr. Sakira Hassan (email@example.com)
Office hours: Please send an email to book an appointment or telco.
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
The course content includes: Probabilistic modeling of dynamic systems, sensor models, batch estimation, Kalman and extended Kalman filtering, bootstrap particle filtering.
Assessment Methods and Criteria
Project work, exercises, and final examination
36 h contact teaching, 97 h independent studies
Roland Hostettler and Simo Särkkä: Lecture notes on Basics of Sensor Fusion; Gustafsson: Statistical Sensor Fusion (2012)
Basics of linear algebra and calculus, basic programming knowledge (MATLAB or Python), basics of statistics.
SDG: Sustainable Development Goals
9 Industry, Innovation and Infrastructure