Course - Nonlinear State Estimation - TK8102
TK8102 - Nonlinear State Estimation
Lessons are not given in the academic year 2020/2021
The course is given every second year, next time in the Spring 2022. 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.
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.
Recommended previous knowledge
TK4150 Nonlinear Control Systems/TTK4150 Linear Systems Theory. Knowledge of observers, Kalman filter, statistics and stochastic processes.
A collection of papers, which will be given at the beginning of the semester.
Credits: 7.5 SP
Study level: Doctoral degree level
Language of instruction: English
- Engineering Cybernetics
Department with academic responsibility
Department of Engineering Cybernetics
- * 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"