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

Postgraduate doctoral study programme

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
45 Lectures
0 Exercises
0 Laboratory exercises
0 Project laboratory