The exercises are all part of a typical application theme, namely tracking, navigation and SLAM: • Bayesian estimation applied to beacon based measurement systems • Kinematic and dynamic models for tracking • Tracking based on discrete Kalman filtering for linear-Gaussian systems • Tracking with extended Kalman filtering in nonlinear systems • Tracking with particle filtering in nonlinear systems • Slam

As such the exercises cover the following theoretical subjects:

1. Fundamentals of parameter estimation; static and scalar case
2. Unbiased linear minimum mean square estimation; static and scalar case
3. Unbiased linear minimum mean square estimation; static and vectorial case
4. Propagation of uncertainty in Gaussian-linear systems; prediction
5. Discrete Kalman filtering
6. Extended Kalman filtering
7. Particle filtering
8. SLAM

Certain projects are still on progress.