Course content

In this course, you learn about mathematical descriptions of stochastic?systems, and how we can use information about the system, mathematical models as well as measurements, to estimate the state of the system. We concentrate primarily on linear systems in the time domain, for which the Kalman filter is the optimal estimator. We present equations, properties and applications of the discrete and continuous Kalman filter. As part of the course, you will also complete a project assignment, in which you will implement and test a Kalman filter on a computer.

Learning outcome

After completing the course, you will

  • have a basic knowledge of state space systems driven by stochastic white noise
  • be able to model, simulate and analyze such systems
  • have basic knowledge of the properties and applications of the Kalman filter
  • know how to design and analyze both optimal and suboptimal Kalman filters for linear stochastic systems
  • know how to use the Kalman filter for nonlinear problems

Admission to the course

Students admitted at UiO must