Skip to main content
MyCourses MyCourses
  • Schools
    School of Arts, Design, and Architecture (ARTS) School of Business (BIZ) School of Chemical Engineering (CHEM) –sGuides for students (CHEM) – Instructions for report writing (CHEM) School of Electrical Engineering (ELEC) School of Engineering (ENG) School of Science (SCI) Language Centre Open University Library Aalto university pedagogical training program UNI (exams) Sandbox
  • CORONAVIRUS INFO
    Koronavirus - tietoa opiskelijalle Coronavirus - information for students Coronavirus - information för studerande Koronaviruksen vaikutus opiskeluun: kysymyksiä ja vastauksia Effects of the coronavirus on studies: questions and answers Coronaviruset och studierna: frågor och svar Corona help for teachers
  • Service Links
    MyCourses - Instructions for Teachers - Teacher book your online session with a specialist - Digital tools for teaching - Personal data protection instructions for teachers - Instructions for Students - Workspace for thesis supervision WebOodi Into portal for students Courses.aalto.fi Library Services - Resourcesguides - Imagoa / Open science and images IT Services Campus maps - Search spaces and see opening hours Restaurants in Otaniemi ASU Aalto Student Union Aalto Marketplace
  • ALLWELL?
    Study Skills Support for Studying Starting Point of Wellbeing About AllWell? study well-being questionnaire
  •   ‎(en)‎
      ‎(en)‎   ‎(fi)‎   ‎(sv)‎
  • Toggle Search menu
  • Hi guest! (Log in)

close

ELEC-E8105 - Non-linear filtering and parameter estimation L, 02.01.2017-17.05.2017

  1. Home
  2. Courses
  3. school of ele...
  4. department of...
  5. elec-e8105 - ...
Syllabus

General

  • General

    General

    Lecturers:
    Prof. Simo Särkkä (simo.sarkka@aalto.fi) and Dr. Angel García-Fernández (angel.garciafernandez@aalto.fi).

    Course assistant:

    Dr. Roland Hostettler (roland.hostettler@aalto.fi).

    Please add "ELEC-E8105" to subject when sending mail concerning the course.

    Learning Outcomes: The student understands the Bayesian basis of estimation in non-linear and non-Gaussian systems. The student understands the principles behind approximate filters and smoothers, and is able to use them in practice. Student knows how to estimate parameters online and offline in non-linear systems.

    Contents: Statistical modeling and estimation of non-linear and non-Gaussian systems. Bayesian filtering and smoothing theory. Extended Kalman filtering and smoothing, sigma-point and unscented filtering and smoothing, sequential Monte Carlo particle filtering and smoothing. Adaptive non-linear filtering; ML, MAP, MCMC, and EM estimation of system parameters. Example applications from navigation, remote surveillance, and time series analysis.

    Assessment Methods and Criteria: Final exam, home exercises, and project work. The grade of the course is the maximum of the grades of the examination and project work. You need to pass both the examination and the project work to pass the course. To pass the course, you also need to do at least 3/4 of the home exercises. Furthermore if you do (at least) 7/8 of the exercises, your grade increases by one (1 -> 2, 2 -> 3, 3 -> 4, 4 -> 5).

    Study Material: Särkkä: Bayesian Filtering and Smoothing (2013), handouts.

    Course Homepage: https://mycourses.aalto.fi/course/view.php?id=13507

    Prerequisites: Basics of Bayesian inference, multivariate calculus and matrix algebra. Basic knowledge or ability to learn to use Matlab or Octave is needed for completing the exercises. "ELEC-E8104 Stochastic models and estimation" is recommended, as well as "BECS-E2601 Bayesian data analysis".

    Grading Scale: 0-5

    Language:
    The course will be taught in English in spring 2017.

    Schedule:
    The lecture/exercise schedule below is preliminary and might change during the course. Note that the first lecture is on January 11th and there is no exercise session on that day.

    • 11.1. Overview of Bayesian modeling of time-varying systems
    • 18.1. From linear regression to Kalman filter and beyond
    • 25.1. Bayesian optimal filtering equations and the Kalman filter
    • 1.2. Extended Kalman filter, statistically linearized filter and Fourier-Hermite Kalman filter
    • 8.2. Unscented Kalman filter, Gaussian Filter, GHKF and CKF
    • 22.2. Particle filtering
    • 1.3. Bayesian optimal smoother, Gaussian and particle smoothers
    • 8.3. Bayesian estimation of parameters in state space models
    • 15.3. Recap of the course topics and project work information
    • 8.3. Individual project work starts
    • 5.4. Examination
    • 8.4. Project deadline

    Recall that before each lecture (except the first one), starting at 3:15 PM, there is an exercise session, starting at 2:15 PM, that you should attend as well.


    • icon for activity News forum
    • icon for activity General discussion Forum

Course home

Course home

Next section

Materials►
Skip Upcoming events
Upcoming events
Loading There are no upcoming events
Go to calendar...
  • ELEC-E8105 - Non-linear filtering and parameter estimation L, 02.01.2017-17.05.2017
  • Sections
  • General
  • Materials
  • Assignments
  • Project work
  • Home

Aalto logo

Tuki / Support
  • MyCourses help
  • mycourses(at)aalto.fi
Palvelusta
  • MyCourses rekisteriseloste
  • Tietosuojailmoitus
  • Palvelukuvaus
About service
  • MyCourses protection of privacy
  • Privacy notice
  • Service description
Service
  • MyCourses registerbeskrivining
  • Dataskyddsmeddelande
  • Beskrivining av tjänsten

Hi guest! (Log in)
  • Schools
    • School of Arts, Design, and Architecture (ARTS)
    • School of Business (BIZ)
    • School of Chemical Engineering (CHEM)
    • –sGuides for students (CHEM)
    • – Instructions for report writing (CHEM)
    • School of Electrical Engineering (ELEC)
    • School of Engineering (ENG)
    • School of Science (SCI)
    • Language Centre
    • Open University
    • Library
    • Aalto university pedagogical training program
    • UNI (exams)
    • Sandbox
  • CORONAVIRUS INFO
    • Koronavirus - tietoa opiskelijalle
    • Coronavirus - information for students
    • Coronavirus - information för studerande
    • Koronaviruksen vaikutus opiskeluun: kysymyksiä ja vastauksia
    • Effects of the coronavirus on studies: questions and answers
    • Coronaviruset och studierna: frågor och svar
    • Corona help for teachers
  • Service Links
    • MyCourses
    • - Instructions for Teachers
    • - Teacher book your online session with a specialist
    • - Digital tools for teaching
    • - Personal data protection instructions for teachers
    • - Instructions for Students
    • - Workspace for thesis supervision
    • WebOodi
    • Into portal for students
    • Courses.aalto.fi
    • Library Services
    • - Resourcesguides
    • - Imagoa / Open science and images
    • IT Services
    • Campus maps
    • - Search spaces and see opening hours
    • Restaurants in Otaniemi
    • ASU Aalto Student Union
    • Aalto Marketplace
  • ALLWELL?
    • Study Skills
    • Support for Studying
    • Starting Point of Wellbeing
    • About AllWell? study well-being questionnaire
  •   ‎(en)‎
    •   ‎(en)‎
    •   ‎(fi)‎
    •   ‎(sv)‎