Summary of Study |
Summary of Branches |
All Subject Groups |
All Subjects |
List of Roles |
Explanatory Notes
Instructions
Anotation:
The course provides fundamentals in three main domains of the statistical signal processing: 1) estimation theory, 2) detection theory, 3) optimal and adaptive filtering. The statistical signal processing is a core theory with many applications ranging from digital communications, audio and video processing, radar and radio navigation, measurement and experiment evaluation, etc.
Course outlines:
1a. MVU estimator, Cramer-Rao lower bound, composite hypothesis, performance criteria
1b. Sufficient statistics
1c. Maximum Likelihood estimator, EM algorithm
1d. Bayesian estimators (MMSE, MAP)
2a. Hypothesis testing (binary, multiple, composite)
2b. Deterministic signals
2c. Random signals
| 3. | | Optimal and adaptive Filtration |
3a. Signal modeling (ARMA, Padé approximation, ...)
3b. Toeplitz equation, Levinson-Durbin recursion
3c. MMSE filters, Wiener filter.
3d. Kalman filter.
3e. Least Squares, RLS
3f. Steepest descent and stochastic gradient algorithms.
3g. Spectrum estimation
Exercises outline:
Literature:
| 1. | | Steven Kay: Fundamentals of Statistical Signal Processing - Estimation theory |
| 2. | | Steven Kay: Fundamentals of Statistical Signal Processing - Detection theory |
| 3. | | Monson Hayes: Statistical digital signal processing and modeling |
| 4. | | Ali Sayed: Fundamentals of Adaptive Filtering |
| 5. | | S. M. Kay: Fundamentals of statistical signal processing-detection theory, Prentice-Hall 1998 |
Requirements:
Subject is included into these academic programs:
| Page updated 7.6.2023 17:50:17, semester: L/2022-3, Z/2023-4, Z/2024-5, Send comments about the content to the Administrators of the Academic Programs |
Proposal and Realization: I. Halaška (K336), J. Novák (K336) |