General
Welcome to the course Basics of Sensor Fusion in Autumn 2022. The Lectures of the course are in lecture hall TU2 on Tuesdays at 12-14 and the Exercises in lecture hall AS2 on Fridays at 12-14. The first lecture is on Tuesday 6.9.2022.
Please notice that Lecture = H01 and Exercise = L01 in Sisu and MyCourses (i.e., they are swapped).
Lectures and Exercises
Main lecturer Prof. Simo Särkkä (simo.sarkka@aalto.fi)
Co-lecturer Fatemeh Yaghoobi (fatemeh.yaghoobi@aalto.fi)
Office hours: Please send an email to book an appointment or telco.
Homeworks and Project Work
Fatemeh Yaghoobi (fatemeh.yaghoobi@aalto.fi)
Office hours: Please send an email to book an appointment or telco.
Course Zulip
We use Zulip for online discussion.
Course Zulip: https://sensor-fusion-2022.zulip.aalto.fi
Zulip registration link: https://sensor-fusion-2022.zulip.aalto.fi/join/q32jnxrvie5lcvio4ppn5fa4/
Intended Learning Outcomes
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 filtering algorithms for solving sensor fusion problems.
Assessment Methods and Criteria
Achievement of the intended learning outcomes is assessed through written mid-term exams, homeworks, and project work. The high-level formula for the grade is
final grade = max(exams+homework grade, project grade)
You still must pass both the exams+homework and project!
The exams and homeworks give a total of 100 points, which determine the grade via mapping
- ≥50pts -> grade 1,
- ≥60pts -> grade 2,
- ≥70pts -> grade 3,
- ≥80pts -> grade 4,
- ≥90pts -> grade 5.
Each of the 2 exams gives a maximum of 30 points and the homeworks (10) give 4 points each. The project work grading is clarified in Project work section (the grade is also 1-5 with a few catches).
Study Material
The course is mainly based on lecture notes and handouts that are made available on the course homepage in Reading materials section.
Prerequisites
Basic knowledge of linear algebra, mathematical statistics, and calculus is required. Knowledge of signals and systems, estimation theory, and electronics may come in handy but is not required.