Phelma Formation 2022

Filtering theory - 4PMSFIL9

  • Number of hours

    • Lectures 14.0
    • Projects 0
    • Tutorials 4.0
    • Internship 0
    • Laboratory works 8.0

    ECTS

    ECTS 2.0

Goal(s)

Give basis on mean square linear filtering for the random signals. Methods description with adaptive experimental approaches

Contact Bertrand RIVET, Laurent GIRIN, Florent CHATELAIN

Content(s)

  • 1 : Wiener filtering in the continuous representation
    • Non-causal Wiener filter
    • Causal Wiener filtering: Bode and Shannon approach
  • 2 : Discrete Wiener filtering with finite memory
    • FIR Wiener filter
    • optimal linear prediction and autoregressive models
  • 3 : Adaptive algorithms
  • 4 : Discrete Kalman filtering


Prerequisites
  • Basic signal processing course
  • Random signals and spectral analysis course
    • notions of power spectral density, Wiener-Khintchine theorem
    • non-parametric spectral estimation: periodogram
  • Notions of optimization: quadratic optimization, gradient descent algorithm

Test

First Session 1
- ET1: Written exam (2 hours)
- CC1: Lab preparation + lab reports

Second Session
- ET2: Written exam (2 hours)
- CC1 = CC2 (no make-up possible for continuous assessment)

Exam conditions: One A4 sheet (double-sided, handwritten, personal) is allowed during the written exam (both sessions 1 and 2).



Session 1 : N1 = 50% examen1 + 50% CC1
Session 2 : N2 = 50% examen2 + 50% CC1 (repris de la session 1)

Additional Information

Course list
Curriculum->SICOM->Semester 8

Bibliography

  • Detection, Estimation and Modulation Theory, Part 1, Harry L. VAN TREES Wiley, 1968
  • Optimal Filtering, Brian D. O. Anderson and John B. Moore. Dover Publications, 2005