Statistical signal and data processing


Teacher coordinatorRomain Hérault
Teacher(s)Romain Hérault, Gilles Gasso
TeachingLectures : 10.5h Exercises : 10.5h Practical work : 10.5h
Web site

Aim and objective

  • Introduction to random signal and time series
  • Caraterize the main properties of random signal
  • Explain and describe the notion of parametric signal models and the main linear signal models and their estimation

Outcome learning

  • INSA reference data :
    • Filtrer et modéliser des signaux [3P]
    • Optimiser un modèle [3P]
  • CNISF reference data :
    • J10Q [1P]
    • H30T [2P]
1 - Notion, 2 - Concept, 3 - Application, I - fully, P - incomplete

Course description

  • Introduction to statistical signal
  • Stationarity and non-stationarity properties
  • Statistical description random signals (mean, auto-correlation, covariances)
  • Linear signal models (AR, ARMA) and their least-squares estimation
  • Introduction to state space representation and notion of Kalman filter
  • Aplications.


  • Signal processing
  • Matlab/Scilab programmation


  • Therrien C. W. and M. Tummala, Probability and Random Processes for Electrical and Computer Engineers, Second edition, CRC Press, 2011
  • M. Barret, Traitement statistique du signal, Technosup, Ellipses, 2009.


  • Final exam: 70%,
  • Laboratorey work-Project: 30%