CCO - 03.2.01 - Filtering: Principles and Applications

Total of Credits: 8
Hours for Theoretical Classes: 60
Hours for Exercises or Seminars: 60

Objective

Introduce the main filtering algorithms derived from the Bayes filter. To study filtering applications in various application areas of robotics, developing the mathematical model of the system, its implementation, and the tuning of filter parameters. Practice the programming of these algorithms using real and simulated data.

Catalog Description

  • Bayes filter
  • Gaussian filters: Kalman filter, extended Kalman filters, Unscented Kalman filter and Information filter
  • Nonparametric filters: Histogram filter and Particle filter
  • Implementation Methods
  • Study of linear and nonlinear applications

Main Bibliography

  1. THRUN, S.; BURGARD, W.; FOX, D.; Probabilistic Robotics. MIT Press, 2005.
  2. KIM, P; HUH, L.; Kalman Filter for Beginners: with MATLAB Examples. A-JIN, 2011.
  3. BROWN, R. G.; HWANG, P. Y. C.; Introduction to random signal and applied Kalman filtering: with Matlab exercises and solutions. John Wiley& Sons, 1997.
  4. GREWAL, M. S.; ANDREWS, A. P.; Kalman Filtering: Theory and Practice Using MATLAB. John Wiley& Sons, 2001.
  5. KAILATH, T.; SAYED, A. H.; HASSIBI, B.; Linear Estimation. Prentice-Hall, 2000.