Phelma Formation 2022

Filtering theory - 4PMSFIL9

  • Number of hours

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

    ECTS

    ECTS 1.5

Goal(s)

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

Contact 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

Written exam - 2h



Additional Information

Course list
Curriculum->Engineering degree->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