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 - 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
Additional Information 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
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Date of update March 18, 2019
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