Topic outline

  • Welcome to the course Basics of Sensor Fusion in Autumn 2023. 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 actual lecture is on Tuesday 12.9.2023 but there is a recap exercise session on programming and linear algebra already on Friday 8.9.2023 in AS2.

    Lectures and Exercises

    Main lecturer Prof. Simo Särkkä (

    Co-lecturer Fatemeh Yaghoobi (

    Office hours: Please send an email to book an appointment or telco.

    Homeworks and Project Work

    Fatemeh Yaghoobi (

    Office hours: Please send an email to book an appointment or telco.

    Course Zulip

    We use Zulip for online discussion. 

    Course Zulip:

    Zulip registration:

    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. 


    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.

  • The main course material is the following booklet:

    Roland Hostettler and Simo Särkkä (2020). Lecture notes on Basics of Sensor Fusion. Lecture notes of course ELEC-E8740 - Basics of sensor fusion.

    Additionally, the lecture slides are available at the bottom of the Schedule page. There are also old Zoom videos available in Old Lecture Videos folder from remote teaching era, but the current lectures are no longer recorded.

  • 8.9. Recap of matrix computations and Python

    12.9.  Lecture 1: Course Overview and Introduction to Sensor Fusion
    15.9.  Exercise 1: Sensor Models

    19.9.  Lecture 2: Sensors, Models, and Least Squares Criterion
    22.9.  Exercise 2: Gaussian Distribution and Cost Functions

    26.9.  Lecture 3: Static Linear Models and Linear Least Squares
    29.9.  Exercise 3: Linear Models and Least Squares

    3.10.  Lecture 4: Static Nonlinear Models, Gradient Descent, and Gauss-Newton
    6.10. Exercise 4: Nonlinear Optimization

    10.10. Lecture 5: Gauss-Newton with Line Search and Levenberg-Marquardt Algorithm
    13.10. Exercise 5: Nonlinear Optimization II

    Monday 16.10. at 9-12 First exam -- covers Lectures 1-5

    24.10. Lecture 6: Linear Continuous-Time Dynamic Models & Project work information session
    27.10. Exercise 6: Dynamic Models I

    31.10.  Lecture 7: Nonlinear Continuous-Time Models and Discrete-Time Dynamic Models 
    3.11. Exercise 7: Dynamic Models II

    7.11. Lecture 8: Discretization of Continuous-Time Dynamic Models
    10.11. Exercise 8: Dynamic Models III 

    14.11. Lecture 9: Filtering Problem and Kalman Filtering
    17.11. Exercise 9: Kalman filtering

    17.11. Project work part I DL

    21.11.  Lecture 10: Extended and Unscented Kalman Filtering 
    24.11. Exercise 10: Nonlinear Kalman filtering 

    28.11. (either guest lecture or recap)

    Friday 1.12. at 12-15  Second exam -- covers Lectures 6-10

    15.12. Project work part II DL

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