Skip to main content

Unit information: Statistical Signal and Image Processing in 2021/22

Unit name Statistical Signal and Image Processing
Unit code EENGM0016
Credit points 10
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Professor. Achim
Open unit status Not open
Pre-requisites

EENGM1400

Co-requisites

None

School/department Department of Electrical & Electronic Engineering
Faculty Faculty of Engineering

Description

The aim of this module is to bridge the gap between classical and modern methods of signal analysis, with an emphasis on the processing of stochastic signals. Students will gain an awareness of optimum signal processing methods primarily based on the least squares error criterion. Optimum filter design will be presented, based on the Wiener filter followed by LMS and RLS filter realizations. Three key application uses of this technology are spectrum estimation, noise cancellation and beamforming. These will be covered from a theoretical and application perspective. An introduction to advanced parameter estimation techniques will also be presented.
Elements:

• Introduction; signal classes, stochastic processes, scope, tools, application areas
• Matrix methods and Numerical Linear Algebra; definitions, operations, solution of equations
• Stochastic processes and Parameter Estimation; stationarity, statistics, distributions, orthogonality, introduction to parameter estimation (MMSE, ML, MAP)
• Optimum Least-Squares Filtering; normal equations, Wiener filtering, AR, MA, ARMA models, linear prediction, lattice filters, Kalman filters
• Adaptive Digital Filters; structure and configurations, performance criteria, error surface searching, gradient methods, LMS, RLS, convergence, fast algorithms and numerical stability
• Model Selection and Parameter Estimation; least squares, Bayesian, maximum likelihood
• Spectral Estimation; DFT, FFT (periodogram), model based techniques (MA, AR, ARMA)
• Adaptive Noise Cancelling; reference signal, output SNR, leakage, applications
• Adaptive Beamforming; conventional methods, adaptive methods, constrained and unconstrained methods, direction of arrival estimation

Intended learning outcomes

On completing this unit, the student will be able to:
1. Quantify the limitations of conventional methods of spectrum estimation.
2. Implement superior model-based algorithms.
3. Design and realise optimum code adaptive digital filters for a range of application scenarios including noise cancellation, linear prediction and beamforming.

Teaching details

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions, problem sheets and self-directed exercises.

Assessment Details

ILOs will be assessed via an exam.

Resources

If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. EENGM0016).

Feedback