Random Signals and Processes

Data is displayed for academic year: 2023./2024.

Course Description

The course gives knowledge of the theory of random signals and processes, as well as its applications in systems for signal processing and analysis. Continuous and discrete random signals. Random processes. Stationarity and independence. Correlation functions and power spectral density. Wiener-Kinchin relations. Random process in linear systems. Signal parameters estimation. Detection. System identification using cross correlation. Noise modeling and characterization. Noise factor. Optimum linear systems. Wiener filter. Matched filter. Kalman filter. Realization of the optimum systems. Signal extraction from the noise by correlation and using matched filter. Signal quantization. Applications in communication, automation and measurements.

Study Programmes

University graduate
[FER3-EN] Data Science - profile
Recommended elective courses (3. semester)

Learning Outcomes

  1. Apply knowledge of mathematics, physics, natural sciences, electrical engineering and computing, and similar and multidisciplinary research areas
  2. Define and describe concepts of stochastic processes in systems
  3. Define and explain use of theory of stochastic processes
  4. Use best practise and develop standards for using appropriate procedures, skills and contemporaary tools in practical applications
  5. Combine acquired knowledge and propose a solution to the given problem
  6. Evaluate a practical solution obtained using stochastic processes

Forms of Teaching


Live lectures, on-line lectures and recordings


Live problem solving sessions, on-line sessions and recordings


Exercises are based on individual preparation work, group work in the laboratory, writing and handing in the reports.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 50 % 20 % 50 % 20 %
Seminar/Project 20 % 20 % 20 % 20 %
Mid Term Exam: Written 20 % 30 % 0 %
Final Exam: Written 20 % 30 %
Exam: Written 50 % 60 %

The threshold on the sum of the midterm and the final exam is 50%.

Week by Week Schedule

  1. Finite-dimensional distributions of processes, Moments; correlation and covariation functions
  2. Classes of processes: Markov, homogenous Markov, weak/strong stationary, independent increment processes, Transition and density matrix and Chapman-Kolmogorov equation for Markov processes.
  3. Correlation and covariance functions of random processes, Fourier transform of stochastic process; Power spectrum density (PSD); Wiener-Kinchin relations
  4. Fourier transform of stochastic process; Power spectrum density (PSD); Wiener-Kinchin relations
  5. Minimal mean-squared error estimation; Linear estimation of random processes; Prediction
  6. Behavior of RP in LTI systems, Second order transfer function
  7. System identification
  8. Midterm exam
  9. Scalar and vector quantization, Quantization noise, Uniform quantization
  10. Optimum quantization
  11. Wiener filter, Description of Wiener filtering
  12. Matched filter
  13. Kalman filter
  14. Applications in signal and image restoration, Applications in radar and sonar
  15. Final exam


H. Stark, J. W. Woods (2002.), Probability and Random Processes with Applications to Signal Processing, 3rd Ed., Prentice Hall
Peyton Z. Peebles, Bertram Emil Shi (2015.), Probability, Random Variables, and Random Signal Principles, McGraw-Hill
S. Lončarić, D. Seršić (2021.), Slučajni signali i procesi: bilješke s predavanja, FER, FER
Tomislav Petković, Damir Seršić, Sven Lončarić (2005.), Zbirka zadataka iz slučajnih procesa, FER

For students


ID 223746
  Winter semester
L1 English Level
L2 e-Learning
30 Lectures
0 Seminar
15 Exercises
8 Laboratory exercises
0 Project laboratory

Grading System

88 Excellent
75 Very Good
62 Good
51 Sufficient