Course: Adaptive Filtering credits: 5

Course code
SEVM21AF
Name
Adaptive Filtering
Study year
2024-2025
ECTS credits
5
Language
English
Coordinator
B.D. Williams
Modes of delivery
  • Lecture
  • Project-based learning
Assessments
  • Adaptive Filtering - Assignment

Learning outcomes

At the end of this module the student is able to: 

  • Model and simulate (sensor) systems using typical computer-based tools.  

  • Clean data using advanced filter techniques, including the Wiener Filter; (normalized) Least-Mean-Square adaptive filters; Recursive Least Squares Adaptive Filters; and Kalman filtering.  

  • Describe systems in the time-domain and/or frequency domain using stationary discrete-time stochastical processes and models, such as autoregressive and moving-average models. 

Content

The course covers the following topics: stationary discrete-time stochastical 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)

  • Institute of Engineering
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