Explanation of basic concepts relating to the processing and analysis of signals and their use to obtain a description of a deterministic or stochastic system for the purpose of automatic control. The experimental identification of systems that can be described by linear models is explained in more detail.

• Basic concepts of signal processing and system identification
• Analog and digital filters, classification and their implementation
• Linear time invariant continuous and discrete models
• Discretization, sampling, frequency properties, anti-aliasing filter
• Parametrization of step and frequency responses
• Concepts of probability theory and stochastic processes
• Characteristics of stochastic processes
• Continuous and discrete Fourier Transform
• Power spectrum, periodogram, white noise
• Identification of dynamic systems in the time domain (linear regression)
• Identification of dynamic systems in the frequency domain
• Time-discrete parametric stochastic models of signals and systems
• Kalman filter - optimal filtration based on the internal description of the system

Solution of 3 projects which are aimed at
1) analytical identification
2) structure and parameter estimation of linear process through the use of deterministic input signal
3) experimental identification of stochastic process

• Hofreiter, M.: Identifikace systémů I, ČVUT v Praze, 2009, 202 s. ISBN 978-80-01-04228-1
• https://moodle.fs.cvut.cz/course/view.php?id=44
• E. W. Kamen and B. S. Heck: Fundamentals of Signals and Systems (2nd Edition), Prentice Hall, 2006
• L. Ljung: System Identification: Theory for the User (2nd Edition), Prentice Hall, 1999
• O. Alkin: Signals and Systems (1st Edition), Taylor & Francis Group, 2014

It is expected prior knowledge of basic control engineering and experience with Matlab.

signal, system, system identification, filtering, probability, stochastic process