Nonlinear filtering (5 credits)
lecturer: Professor Miroslav
Simandl, University of West Bohemia in Pilsen,
lastname@kky.zcu.cz
schedule: Lectures and
tutorials 3hrs/day (9am-noon) over 10 weekdays starting Monday 7 Aug
and ending Friday 18
Aug 2006.
place: Tampere University of
Technology, Tietotalo building, room Tb214.
topics:
- Aug 7
- Structural and probabilistic modelling, parameter estimation,
state estimation, the Bayesian approach, statistical approach, the
Bayesian recursive relation as a general solution of state estimation
problem for stochastic system in discrete state space representation
- Aug 8
- State estimation of linear Gaussian system, derivation of the
Kalman filter from the Bayesian recursive relation, linear filtering,
what happens in case of nonlinear system or nongaussian random
variables, classification of nonlinear filters, local and global
filters
- Aug 9
- The Extended Kalman Filter, the Iteration Filter, the Second
Order Filter
- Aug 10
- Derivative-free filters: the Unscented Kalman filter, the
Difference filter
- Aug 11
- Examples
- Aug 14
- Analytical approach, the Gaussian sum filter, Gaussian sum
representation, linear system with nongaussian noises, nonlinear system
with Gaussian noises, special cases, abrupt changes of parameters,
outliers
- Aug 15
- Simulation approach, simulation Monte Carlo methods, particle
filters
- Aug 16
- Multi model approach and the Gaussian sum method, applications
- Aug 17
- Numerical approach, basic and advanced point mass methods
- Aug 18
- The Cramer Rao bounds as a tool for quality evaluation of the
nonlinear filters
prerequisite:
SGN-2606
or equivalent
literature: course notes, lecture
audio files
credit requirements: written
exam (Aug 24, 1pm, Tb214)
course registration: please
contact the course coordinator Robert Piché <
> .