Postgraduate Course: Statistical Signal Processing (PGEE11027)
Course Outline
| School | School of Engineering |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 20 |
| Home subject area | Postgrad (School of Engineering) |
Other subject area | None |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | This course introduces the fundamental statistical tools that are required to analyse and describe advanced signal processing algorithms. It provides a unified mathematical framework in which to describe random events and signals, and how to describe key characteristics of random processes. It investigates the affect of systems and transformations on time-series, and how they can be used to help design powerful signal processing algorithms. Finally, the course deals with the notion of representing signals using parametric models; it covers the broad topic of statistical estimation theory, which is required for determining optimal model parameters. Emphasis is placed on relating these concepts to state-of-the art applications and signals. This module provides the fundamental knowledge required for the advanced signal, image, and communication courses in the MSc. course. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | None |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
| Not being delivered |
Summary of Intended Learning Outcomes
| At the end of this module, a student should be able to: define, understand, and manipulate scalar and multiple random variables using the theory of probability; explain the notion of characterising random variables using moments, and be able to manipulate them; explain, describe, and understand the notion of a random process and statistical time series; characterise random processes in terms of its statistical properties, including the notion of stationarity and ergodiciy; define, describe, and understand the notion of the power spectral density of stationary random processes; analyse and manipulate power spectral densities; analyse in both time and frequency the affect of transformations and linear systems on random processes, both in terms of the density functions and statistical moments; explain the notion of parametric signal models, and describe common regression-based signal models in terms of its statistical characteristics and in terms of its affect on random signals; discuss the principles of estimation theory, define basic properties of estimators, and be able to analyse and calculate the properties of a given estimator; apply least squares, maximum-likelihood, and Bayesian estimators to model based signal processing problems. |
Assessment Information
| 100% open-book formal written examination |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | Probability, scalar and multiple random variables, stochastic processes, power spectral densities, l |
Contacts
| Course organiser | Dr James Hopgood
Tel: (0131 6)50 5571
Email: |
Course secretary | Mrs Sharon Potter
Tel: (0131 6)51 7079
Email: |
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