Statistical Signal Processing

B232 - Summer 23/24
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Statistical Signal Processing - B2M37SSP

Student survey results
Credits 5
Semesters Summer
Completion Exam
Language of teaching Czech
Extent of teaching 4P+0C
Annotation
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.
Study targets
No data.
Course outlines
1. Estimation
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)
2. Detection
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 outlines
No data.
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
No data.