Počet kreditů 6
Vyučováno v Winter
Rozsah výuky 2P+2C
Garant předmětu
Přednášející
Cvičící

This course provides a basic discourse on adaptive algorithms for filtering, decorrelation, separation and beamforming.

The knowledge of basic digital signal processing techniques - primarily the spectral analysis and non-adaptive linear filtering. Ability to use Matlab.

This course aims to provides the basic knowledge in the area of algorithms for filtering, decorrelation, separation and beamforming.

1. Block algorithms for estimation
2. Block algorithms for prediction
3. LMS and RLS algorithms and their use for estimation and prediction
4. Convergence of LMS and RLS algorithms
5. Structures for implementation of adaptive filters
6. Use of adaptive algorithms for signal compression
7. Use of adaptive algorithms for noise suppression
8. Kalman filters
9. Grid filters and particle filters
10. Adaptive algorithms for decorrelation of multidimensional signals
11. Adaptive algorithms for separation of multidimensional signals
12. Adaptive beamforming - LCMV and MVDR algorithms
13. Adaptive beamforming - MUSIC algorithm
14. Reserved

1. Implementation of block algorithms for estimation
2. Implementation of block algorithms for prediction
3. Implementation of LMS and RLS algorithms
4. Convergence of LMS and RLS algorithms
5. Comparisoin of structures for implementation of adaptive filters
6. Vocoder
7. Adaptive supression of narrowband interference.
8. Application of Kalman filters
9. Use of grid filters and particle filters
10. Implementation of algorithms for decorrelation of multidimensional signals
11. Implementation of algorithms for separation of multidimensional signals
12. Application of LCMV and MVDR algorithms
13. Application of MUSIC algorithm
14. Reserved

Sayed, A.H., Adaptive Filters, Wiley-IEEE Press, 2008.
Bellanger, M.B., Adaptive Digital Filters, Marcel Dekker, NY 2001.
Hyvarinen, A, Karhunen, J, Oja, E. Independent Component Analysis, John Wiley & Sons, 2004.

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