CTU FEE Moodle
Statistical Signal Processing
B232 - Summer 23/24
This is a grouped Moodle course. It consists of several separate courses that share learning materials, assignments, tests etc. Below you can see information about the individual courses that make up this Moodle course.
Statistical Signal Processing - A8B37SSP
Main course
Credits | 6 |
Semesters | Summer |
Completion | Assessment + Examination |
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
The course provides theoretical foundations in the three main areas of stochatical signal processing and offers a unifying view of seemingly different approaches.
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 analysis and 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 analysis and estimation
Exercises outlines
The course has only lectures
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
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
Requirements
None
Statistical Signal Processing - B2M37SSP
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
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
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.
Statistical Signal Processing - B2M37SSPA
Credits | 6 |
Semesters | Summer |
Completion | Assessment + Examination |
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
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
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.