Course: Adaptive Filters credits: 5
- Course code
- SEVM21AF
- Name
- Adaptive Filters
- Study year
- 2026-2027
- ECTS credits
- 5
- Language
- English
- Coordinator
- B.D. Williams
- Modes of delivery
-
- Lecture
- Project-based learning
- Assessments
-
- Adaptive Filters - Assignment
Learning outcomes
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.
Content
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.
Included in programme(s)
School(s)
- Engineering, Life Sciences & ICT