ELEC6229 Advanced Systems and Signal Processing
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.
Aims and Objectives
To provide an introduction to advanced model based signal processing methods and systems design theories.
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 task||37|
|Preparation for scheduled sessions||18|
|Wider reading or practice||29|
|Total study time||150|
Resources & Reading list
Graham C. Goodwin, Stefan F. Graebe and Mario E. Salgado (2001). Control System Design.
Dan Simon (2006). Optimal State Estimation: Kalman, H-infinity, and Nonlinear Approaches.
James V. Candy (2005). Model-Based Signal Processing.
Repeat type: Internal & External