TK8102 - Nonlinear State Estimation


Examination arrangement

Examination arrangement: Oral examination and Report
Grade: Passed/Failed

Evaluation form Weighting Duration Examination aids Grade deviation
Approved report 50/100
Oral examination 50/100 D

Course content

The course is given every second year, next time in the Spring 2020. The course presents state estimation techniques for nonlinear dynamic systems. The course will provide a theoretic foundation and skills in design of both deterministic and probabilistic estimation for nonlinear systems based on analysis of the system and its observability properties. The course is given in English.

Learning outcome

KNOWLEDGE: • A thorough knowledge of theory and methods for state estimation of deterministic and stochastic nonlinear dynamical systems • Relevant definitions and properties of observability • Observers for nonlinear dynamical systems: Filter structures, nonlinear separation principles and design methods • Kalman-based techniques for stochastic systems: Bayesian formulation, error-state Kalman filter and canonical form • The Cramer-Rao bound and filter consistency • Particle filters, Markov Chain Monte Carlo and other sampling based methods • Data association • Graphical models • Parameter estimation • Data association • Applications in sensor fusion SKILLS: • Proficiency in analyzing the observability properties of nonlinear dynamical systems • Proficiency in independently assessing the advantages and disadvantages of different estimation methods, and make a qualified choice of method for a given system • Proficiency in independently applying the different methods for estimator design • Proficiency in assessing the advantages and limitations of the resulting system GENERAL COMPETENCE: • Skills in applying this knowledge and proficiency in new areas and complete advanced tasks and projects • Skills in communicating extensive independent work, and master the technical terms of nonlinear state estimation • Ability to contribute to innovative thinking and innovation processes

Learning methods and activities

Study groups and optional problem sets. Project with report.

Course materials

A collection of papers, which will be given at the beginning of the semester.

More on the course



Version: 1
Credits:  7.5 SP
Study level: Doctoral degree level


Term no.: 1
Teaching semester:  SPRING 2020

No.of lecture hours: 3
Lab hours: 2
No.of specialization hours: 7

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Engineering Cybernetics
Contact information
Course coordinator:

Department with academic responsibility
Department of Engineering Cybernetics



Examination arrangement: Oral examination and Report

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Autumn ORD Oral examination 50/100 D
Room Building Number of candidates
Spring ORD Oral examination 50/100 D
Room Building Number of candidates
Autumn ORD Approved report 50/100
Room Building Number of candidates
Spring ORD Approved report 50/100
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date.
If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

More on examinations at NTNU