Stochastic signals

In this course, you will study random signals and processes, key concepts such as stationarity, correlation and spectral density, and how linear systems respond to stochastic inputs. You will also work with fundamental techniques for estimation, filtering and prediction — including methods that form the basis of modern signal processing algorithms. 

Knowledge of stochastic signals is essential in many high-tech sectors. In wireless communications, engineers model noise and fading to design reliable 5G/6G systems. In radar, aerospace and defense, statistical signal processing is used for detection and tracking in complex environments. In autonomous vehicles and robotics, stochastic models enable state estimation and navigation. Applications also extend to medical technology, remote sensing, AI, and financial time-series analysis. 

This course is ideal for master’s students aiming for careers in telecommunications, space and aerospace industries, automotive sensing, advanced analytics, or research. It strengthens both your mathematical maturity and your ability to apply theory to real engineering problems — valuable skills for industry and doctoral studies alike.