Course - Nonlinear State Estimation - TK8102
Nonlinear State Estimation
Choose study yearAssessments and mandatory activities may be changed until September 20th.
About
About the course
Course content
The course is given spring in even-numbered years. The course presents state estimation techniques for nonlinear dynamic systems with an additional focus on Multiple Target Tracking (MTT) and Simultaneous Localization And Mapping (SLAM) methods, the underlying theoretic foundations and implementation skills. The course is given in English.
Learning outcome
KNOWLEDGE: * Thorough knowledge of theory and methods for state estimation of stochastic and deterministic nonlinear dynamical systems * Observability * State estimation for stochastic systems: Optimization, sigma-points and Monte-Carlo techniques. * State estimation for deterministic systems: Nonlinear observers. * Lie groups in navigation and SLAM * Gaussian processes * Outlier rejection and data association 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 and tracking 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 a similar course that covers Kalman filter, stochastic system theory and estimation.
Course materials
A collection of papers, which will be given at the beginning of the semester.
Subject areas
- Engineering Cybernetics