UNIK4560 – Applied parameter and state estimation
Schedule, syllabus and examination date
Course content
System identification: Observability, parameter estimation using augmented Kalman filter and the maximum likelihood method. Bayes estimation in nonlinear non-gaussian systems: optimal solution, point mass and particle filters. Hidden Markov models (HMM): simulation, prediction, filtering, smoothing and parameter estimation. The multiple-model estimation algorithm for static and dynamic systems used for parameter estimation and target trackning. Algortihms: optimal, pseudo bayesian and IMM.
Learning outcome
The student will gain a thorough knowledge of methods used for parameter estimation in state space models and state estimation in nonlinear non-gaussian dynamic systems. The methods will be applied to navigation and tracking problems.
Admission
Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.
If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.
Prerequisites
Formal prerequisite knowledge
None.
Recommended previous knowledge
MAT1110-Kalkulus og lineær algebra, MAT1120-Lineær algebra, MAT-INF1310-Ordinary differential equations, MAT2310-Optimal kontrollteori, STK1100-Sannsynlighetsregning og statistisk modellering, UNIK4500 – Stokastiske systemer.
Teaching
Three hours of lectures and one houre of problem solving sessions per week in the autumn term. In the spring term the course is offered as a guided self-tuition course. The student will get a DVD containing videos of the lectures and copies of all written material including what was written on the whiteboard. The students must hand in and pass on mandatory project task before they are admitted to take the exam.
Examination
Oral or written examination. Graded marks.