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DRPS : Course Catalogue : School of Engineering : Postgrad (School of Engineering)

Postgraduate Course: Adaptive Signal Processing (PGEE11019)

Course Outline
SchoolSchool of Engineering CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis 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 1. Random signals
2. Cross correlation and spectral factorization
3. Filter noise calculations
4. Principle of statistical orthogonality
5. The Wiener finite impulse response (FIR) filter
6. The scalar Kalman filter
7. The vector Kalman Filter
8. The Kalman Filter for tracking systems
9. Adaptive filters: least squares and recursive least squares
10. The least mean squares algorithm
11. Comparison of adaptive filter algorithms
12. Applications in equalisation and echo cancellation
13. Adaptive whitened matched filter case study
14. Review of classical spectral analysis
15. Autoregressive spectral analysis
16. Linear predictive coding of speech
17. Spatially variant apodization
18. The amplitude and phase estimation algorithm
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)
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
High Demand Course? Yes
Course Delivery Information
Academic year 2019/20, 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 )
Assessment (Further Info) Written Exam 100 %, Coursework 0 %, Practical Exam 0 %
Additional Information (Assessment) 100% closed-book formal written examination
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:
  1. perform simple spectral factorization tasks and calculate noise component at output of discrete time filters.
  2. - derive and apply the principle of statistical orthogonality and design Wiener infinite impulse response (IIR) filters
  3. - derive the scalar Kalman filter and apply the vector Kalman filter -
  4. - 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
  5. derive and apply the spatially variant apodization (SVA).
Reading List
Additional Information
Graduate Attributes and Skills Not entered
Additional Class Delivery Information 2 lectures and 1 tutorial per week
Keywordsspectral analysis,spectral estimation,signal detection,adaptive filters,least squares methods
Course organiserProf Bernie Mulgrew
Tel: (0131 6)50 5580
Course secretaryMrs Megan Inch-Kellingray
Tel: (0131 6)51 7079
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