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Our engineering & Master degrees

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Filtering theory - 4PMSFIL9

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  • Number of hours

    • Lectures : 14.0
    • Tutorials : 4.0
    • Laboratory works : 0
    • Projects : 0
    • Internship : 0
    ECTS : 1.5

Goals

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

Contact Florent CHATELAIN

Content

  • Chapter 1: Continuous Wiener Filter
  • Chapter 2: Discrete Wiener Filter, Adaptive algorithms
  • Chapter 3: Kalman Discrete Filter.


Prerequisites

Basic on deterministic and stochastic Signal Processing.

Tests

Written exam - 2h



Additional Information

Curriculum->Master TSI SIGMA->Semester 8
Curriculum->SICOM->Semester 8
Curriculum->Master->Semester 8

Bibliography

  • Detection, Estimation and Modulation Theory, Part 1, Harry L. VAN TREES Wiley, 1968
  • Kalman Filtering CHANG & CHEN
  • Extraction of Signal From Noise, WAINSTEIN & ZUBAKOV, Prentice HALL

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Date of update March 18, 2019

Grenoble INP Institut d'ingénierie Univ. Grenoble Alpes