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13E053SSE - Stochastic Systems and Estimation

Course specification
Course title Stochastic Systems and Estimation
Acronym 13E053SSE
Study programme Electrical Engineering and Computing
Module Signals and Systems
Type of study bachelor academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
    ESPB 6.0 Status mandatory
    Condition Digital signal processing
    The goal Introduce students to basics of stochastic signals and systems, and common estimation procedures. Convey elementary principles by which discrete and continuous stochastic systems operate. Enable students to implement well known estimation procedures on a computer.
    The outcome After passing the exam, students will have the basic skills to properly analyze a stochastic system and will be able to implement and deploy algorithms for parameter estimation.
    Contents
    Contents of lectures Stochastic processes, stationary and ergodic processes, white random processes, spectral representation of stochastic processes, linear filtering, spectral factorization, optimal non-recursive estimators, Wiener filter, Kalman filter, non-linear minimum-variance estimators.
    Contents of exercises Students have obligation to solve several homework assignments using the most common programming frameworks for estimation.
    Literature
    1. Modern Spectral Estimation: Theory and Application, Steven Kay, Prentice Hall, 1998
    2. Signals, Systems, and Transforms, Charles Phillips, John Paar, Eve Riskih, Prentice Hall, 2003.
    Number of hours per week during the semester/trimester/year
    Lectures Exercises OTC Study and Research Other classes
    3 2
    Methods of teaching 45 hours of lectures + 15 hours of auditory exercises + 15 hours of practical exercises with computers
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures 0 Test paper 0
    Practical lessons 40 Oral examination 60
    Projects
    Colloquia 0
    Seminars 0