Estimation in Nonlinear Systems

Bayes estimation in nonlinear, non-gaussian systems:
Nonlinear discret-time state space models (unscented Kalman filter, point-mass and particle filters). Hidden markov models (HMM) (forward, backward, Viterbi and Baum-Welch algorthms). Static and dynamic multiple models (MMEA, GPB1, GPB2 and IMM algorthms).
The methods will applied to navigation and tracking problems.Contact the lecturer for more information.

KNOWLEDGE: Know the latest methods used for estimating a vehicles position, velocity and attitude in a modern navigation system.
SKILLS: Independent management of small R&D projects and contribute actively in larger projects.
GENERAL COMPETENCE: communicate work related problems with specialists and nonspecialists.

Lectures, problem sets and project assignments.