Vak: Adaptive Filters credits: 5
- Vakcode
- SEVM21AF
- Naam
- Adaptive Filters
- Studiejaar
- 2026-2027
- ECTS credits
- 5
- Taal
- Engels
- Coördinator
- B.D. Williams
- Werkvormen
-
- Hoorcollege
- Projectonderwijs
- Toetsen
-
- Adaptive Filters - Opdracht
Leeruitkomsten
At the end of this module the student is able to:
- understand the link between Fourier analysis and the Z-transform.
- create and understand how a digital filter (FIR/IIR) works.
- clean data using advanced filtering techniques, including the Wiener filter, (normalized) Least-Mean-Square (LMS) adaptive filters, Recursive Least Squares (RLS) adaptive filters, and Kalman filtering.
- describe systems in the time-domain and/or frequency domain using stationary discrete-time stochastic processes and models, such as autoregressive (AR) and moving-average (MA) models.
Inhoud
The course covers the following topics:
Z-transform, FIR and IIR filters, stationary discrete-time stochastic processes and models, (normalized) Least-Mean-Square adaptive filters, Recursive Least Squares Adaptive Filters and Kalman filtering, model simulation tools, model-based reasoning.
Z-transform, FIR and IIR filters, stationary discrete-time stochastic processes and models, (normalized) Least-Mean-Square adaptive filters, Recursive Least Squares Adaptive Filters and Kalman filtering, model simulation tools, model-based reasoning.
Opgenomen in opleiding(en)
School(s)
- Engineering, Life Sciences & ICT