# Subject description - BE2M37SSP

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 BE2M37SSP Statistical Signal Processing Extent of teaching: 4p+0 Guarantors: Roles: Language ofteaching: EN Teachers: Completion: ZK Responsible Department: 13137 Credits: 5 Semester: L

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:

 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 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:

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 Page updated 24.2.2020 05:51:51, semester: Z,L/2020-1, Z,L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)