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.
Credits: 5
Schedule: 08.09.2023 - 01.12.2023
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Simo Särkkä, Lauri Palva
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
The course content includes: Least squares estimation, modeling of dynamic systems, sensor models, optimization methods, Kalman and extended Kalman filtering.
Assessment Methods and Criteria
valid for whole curriculum period:
Project work, exercises, and final examination
Workload
valid for whole curriculum period:
36 h contact teaching, 97 h independent studies
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Autumn I - II
2023-2024 Autumn I - II