Postgraduate Course: Adaptive Signal Processing (PGEE11019)
Course Outline
| School | School of Engineering |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course deals with adaptive filters and related linear estimation techniques such as the Wiener finite impulse response filter and Kalman filters. The concepts of training and convergence are introduced and the trade-off between performance and complexity is considered. The application of these techniques to problems in equalization, coding, spectral analysis and detection is examined. |
| Course description |
The following topics are covered:
- spectral factorization , inverse filters & filter noise calculations;
- the principle of statistical orthogonality and the Wiener finite impulse response (FIR) filter;
- recursive least squares and least mean squares adaptive filter algorithms and their application;
- the scalar Kalman filter, the vector Kalman Filter and application to tracking systems;
- autoregressive spectral analysis and linear predictive coding of speech;
- spatially variant apodization and amplitude and phase spectral analysis .
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
It is RECOMMENDED that students have passed
Statistical Signal Processing (PGEE11027) AND
Discrete-Time Signal Analysis (PGEE11026)
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | Compulsory book purchase (from £57.99): B. Mulgrew, P.M. Grant, and J.S. Thompson, Digital Signal Processing: Concepts and Applications (Second Edition, 2002), Macmillan Education UK, ISBN: 9780333963562 |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2023/24, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 11,
Formative Assessment Hours 1,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
62 )
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| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% Examination
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| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- perform simple spectral factorization tasks and calculate noise component at output of discrete time filters.
- derive and apply the principle of statistical orthogonality and design Wiener finite impulse response (FIR) filters
- derive the scalar Kalman filter and apply the vector Kalman filter
- derive the least mean squares (LMS) and recursive least squares (RLS) adaptive filter algorithms and apply them to problems in system identification, linear predication and equalization
- derive and apply some modern adaptive-filter-based spectral analysis techniques.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Additional Class Delivery Information |
2 lectures and 1 tutorial per week |
| Keywords | spectral analysis,spectral estimation,signal detection,adaptive filters,least squares methods |
Contacts
| Course organiser | Dr David Laurenson
Tel: (0131 6)50 5579
Email: |
Course secretary | Ms Brunori Viola
Tel: (0131 6)50 5687
Email: |
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