- Docente: Petar Mihailo Djuric
- Credits: 3
- SSD: 0
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Telecommunications Engineering (cod. 9205)
Learning outcomes
The course is intended to extend knowledge on application fields and needs of evolving communication scenarios, to practice and develop interaction skills with lecturer and team working, to stimulate active learning.
Course contents
Review of estimation theory
Bayesian estimation, batch and recursive estimation
State-space models and filtering
Optimal filtering
Kalman and extended Kalman filtering
Unscented Kalman filtering, Quadrature Kalman filtering, and Gaussian sum filtering
Monte Carlo-based methods for estimation of probability densities
Particle filtering
Bayesian smoothing
Curse of dimensionality
Applications
Readings/Bibliography
Papers for additional reading will be provided as the course progresses.
Teaching methods
There will be weekly classes, each three hours long. The instructor will present the material using Power point slides. The teaching will have an interactive component.
Assessment methods
The assessment will be based on projects where students will program Bayesian filtering methods on given applications.
Teaching tools
The instructor will use Matlab to demonstrate the performance of various methods.
Office hours
See the website of Petar Mihailo Djuric