Please note! Course description is confirmed for two academic years, 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.

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
Prerequisites

FURTHER INFORMATION

Further Information
  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Autumn I - II
    2023-2024 Autumn I - II