Applied estimation techniques

Course Description

Estimation problems definition. The role of the estimation techniques in complex systems. State estimation of stochastic systems: linear and nonlinear Kalman filters, particle filters. State estimation in non-Euclidean spaces. Matrix Lie groups. Kalman filter on matrix Lie groups. Application examples: estimation of difficult-to-measure variables, multi-sensor information fusion, rigid body pose estimation in 3D, moving objects tracking.

Study Programmes

Post-graduation study

Literature

Dierk Schröder (2013.), Intelligent Observer and Control Design for Nonlinear Systems, Springer Science & Business Media
Simon Haykin (2004.), Kalman Filtering and Neural Networks, John Wiley & Sons
Arnaud Doucet, Nando de Freitas, Neil Gordon (2013.), Sequential Monte Carlo Methods in Practice, Springer Science & Business Media
Simo Särkkä (2013.), Bayesian Filtering and Smoothing, Cambridge University Press
Yaakov Bar-Shalom, X. Rong Li, Thiagalingam Kirubarajan (2004.), Estimation with Applications to Tracking and Navigation, John Wiley & Sons
Timothy D. Barfoot (2017.), State Estimation for Robotics, Cambridge University Press

General

ID 154823
  Summer semester
6 ECTS
L2 English Level
L1 e-Learning