Filtering theory - 4PMSFIL9

Goal(s)

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

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

• 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