Course - Nonlinear State Estimation - TK8102
TK8102 - Nonlinear State Estimation
About
Examination arrangement
Examination arrangement: Oral examination and Report
Grade: Passed / Not Passed
Evaluation | Weighting | Duration | Grade deviation | Examination aids |
---|---|---|---|---|
Approved report | 50/100 | |||
Oral examination | 50/100 | D |
Course content
The course is given every second year, next time in the Spring 2024. The course presents state estimation techniques for nonlinear dynamic systems with an additional focus on Simultaneous Localization And Mapping (SLAM) methods, the underlying theoretic foundation and implementation skills. 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 * State estimation techniques: Filtering and smoothing * Kalman-based techniques for stochastic systems* Nonlinear observers * Graphical models, Factor-graph based SLAM techniques * 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 designing SLAM systems. 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
Knowledge of observers, Kalman filter, statistics and stochastic processes. TTK4250 Sensor Fusion or TTK4150 Nonlinear Systems can be useful.
Required previous knowledge
TTK4115 Linear Systems Theory or similar (with minimum 20% Kalman filter / stochastic system theory / estimation).
Course materials
A collection of papers, which will be given at the beginning of the semester.
No
Version: 1
Credits:
7.5 SP
Study level: Doctoral degree level
Term no.: 1
Teaching semester: SPRING 2024
Language of instruction: English
Location: Trondheim
- Engineering Cybernetics
Department with academic responsibility
Department of Engineering Cybernetics
Examination
Examination arrangement: Oral examination and Report
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Spring ORD Oral examination 50/100 D
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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"