ELEC-E8740 - Basics of sensor fusion, 10.09.2018-10.12.2018

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  Course home page

  Welcome to the course Basics of Sensor Fusion.

  Lecturer

  Roland Hostettler (roland.hostettler@aalto.fi)
  Office F308, Rakentajanaukio 2
  

  Office hours: Generally between 13.00 and 16.00 on workdays, but requesting an appointment by e-mail beforehand is highly recommended to ensure that I'm available.
  

  Teaching Assistant

  Filip Tronarp (filip.tronarp@aalto.fi)
  Office F322, Rakentajanaukio 2

  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 as well as particle filtering algorithms for solving sensor fusion problems.

  
  

  Assessment Methods and Criteria

  Achievement of the intended learning outcomes is assessed through an individual written exam as well as a group project work.

  To pass the course, you need to:
  

  • pass the written exam,
  • pass the project.

  
  

  Written exam: The written exam is a pen and paper exam. Allowed aids:

  • One (1) hand-written A4 paper with notes (written by yourself, i.e., not written w/ computer, not copied from your peers, etc.)
  • Pens
  • Calculator
  • No lecture notes, books, etc.

  
  

  The grading scale for both the exam and the project is 0-5. The final grade is the average of the written exam and the project.
  

  Study Material

  The course is mainly based on lecture notes and handouts that will be made available on the course homepage. Optionally, the students may also purchase the textbook "Statistical Sensor Fusion" by F. Gustafsson (not mandatory).
  

  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.
  

  Schedule

  Lectures: Lectures are held on Wednesdays, 08:15 - 10:00 (except for the first lecture on Monday, Sep 10, 2018, 14:15 - 16:00) in R037/1199 TU6.

  Preliminary schedule (may be subject to changes):

  Date	Topic	Recommended Reading (Lecture Notes)
  10.9.	Course Overview and Introduction to Sensor Fusion	Chapter 1
  12.9.	Static Linear Models and Linear Least Squares	Chapter 2, Sections 2.1-2.2
  19.9.	Sequential and Regularized Least Squares, Static Nonlinear Models and Gradient Descent	Chapter 2, Sections 2.3-2.4, Chapter 3, Section 3.1
  26.9.	Gauss-Newton and Levenberg-Marquardt Algorithms	Chapter 3, Sections 3.2-3.4
  3.10.	Separable Models and Introduction to State-Space Models	Chapter 3, Section 3.5
  10.10.	Continuous-Time State-Space Models	Chapter 4, Section 4.1
  17.10.	Nonlinear Continuous-Time Models and Discrete-Time Models	Chapter 4, Sections 4.1-4.2
  31.10.	Discretization of Linear Continuous-Time State-Space Models	Chapter 4, Section 4.3
  7.11.	Discretization of Nonlinear Continuous-Time State-Space Models	Chapter 4, Section 4.4
  14.11.	Filtering Problem and Kalman Filtering	Chapter 2, Section 2.4, Chapter 5, Sections 5.1-5.2
  21.11.	Extended and Unscented Kalman Filtering	Chapter 5, Sections 5.3-5.4
  28.11.	Bootstrap Particle Filtering	Chapter 5, Section 5.5
  5.12.	Course Summary	Chapters 1-5

  
  
  

  Exercises: Exercise sessions are held on Mondays, 14:15 - 16.00 in R037/1199 TU6, starting on Monday, Sep 17, 2018.
  

  Exam: The written exam will take place on December 10, 2018, 14.00 - 17.00 in R037/1199 TU6.
  
  

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