At the end of this course, the students; 1) Gain an ability to solve problems. 2) Know and use the probablistic applications and probability theory. 3) Know and use the structure of basic random processes. 4) Gain an ability to calculate density and distribution functions. 5) Know and develop the applications on power spectrum/spectral estimation. 6) Know and use the structures of Adaptive Filters (LMS, RLS, Kalman). 7) Compact the theoretical knowledge with simulations.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
None
COURSE DEFINITION
Discrete-Time Random Processes. Wiener Filtering. Spectrum Estimation (Minimum Variance, Maximum Entropy, MUSIC, PCA Methods). Adaptive Filtering (LMS, RLS, Square-Root Kalman Filters). Tracking of Time-Varying Systems.
COURSE CONTENTS
WEEK
TOPICS
1st Week
Discrete-Time Random Processes.
2nd Week
Discrete-Time Random Processes.
3rd Week
Wiener Filtering.
4th Week
Wiener Filtering.
5th Week
Spectrum Estimation (Minimum Variance, Maximum Entropy, MUSIC, PCA Methods).
6th Week
Spectrum Estimation (Minimum Variance, Maximum Entropy, MUSIC, PCA Methods).
7th Week
Spectrum Estimation (Minimum Variance, Maximum Entropy, MUSIC, PCA Methods).