Nonlinear State Estimation

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.

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

Study groups and optional problem sets. Project with report.