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The University of Southampton

ELEC6229 Advanced Systems and Signal Processing

Module Overview

This module aims to introduce to the students advanced model based signal processing methods and systems design theories, with illustrative case studies to demonstrate how the knowledge obtained in this module can be used in some challenging real life applications. The module uses the specialist computation/simulation tool Matlab.

Aims and Objectives

Module Aims

To provide an introduction to advanced model based signal processing methods and systems design theories.

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Estimate unknown system parameters from noisy measurement data
  • Estimate system state information from noisy measurements
  • Evaluate the performance of a stochastic system using Monte Carlo methods
  • Design and implement model based control systems
  • Apply the model based signal processing and system design methods to real life applications


The course will cover the following topics: Review of mathematical background - Review of state space modelling - Review of linear algebra - Review of probability Stochastic simulation and Monte Carlo method - Random Number Generation - Monte Carlo method Stochastic simulation using Monte Carlo simulation - Stochastic signal processing, focusing on - Estimation problem and least squares - Kalman filtering and Extended Kalman filtering - Particle Filtering Advanced system control theory - Optimal Control: LQR and LQG - Receding horizon methods A case study: next generation health care – electrical stimulation and robotic-assisted upper-limb stroke rehabilitation.

Learning and Teaching

Completion of assessment task37
Preparation for scheduled sessions18
Follow-up work18
Wider reading or practice29
Total study time150

Resources & Reading list

Graham C. Goodwin, Stefan F. Graebe and Mario E. Salgado (2001). Control System Design. 

James V. Candy (2005). Model-Based Signal Processing. 

Dan Simon (2006). Optimal State Estimation: Kalman, H-infinity, and Nonlinear Approaches. 



MethodPercentage contribution
Coursework 20%
Coursework 20%
Coursework 10%
Coursework 20%
Coursework 30%


MethodPercentage contribution
Coursework assignment(s) 100%


MethodPercentage contribution
Coursework assignment(s) 100%

Repeat Information

Repeat type: Internal & External

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