Introduction to Estimation & Kalman Filter
22nd July 2010, Glasgow

This meeting has since been held. (Register your interest for the next course here.)

Some of the presentations may now be available for download on-line (ACTC members only). You can do so from our Download Centre or simply by clicking on the appropriate link in the agenda below.

Please Note: Presentation material downloaded from our web-site should not be incorporated (either in part or in entirety) into other work etc., nor distributed (either in part or in entirety) to third parties, without the express permission of the authors.


This one-day course is aimed at introducing Kalman Filter as an estimator and its application to engineers. Kalman filter is an efficient recursive filter that is capable to estimate a state from a series of measurement of the other states of a linear dynamic system. In order for parameter or state estimation of a nonlinear system, extended Kalman filter is used for this application and this is covered in this course.

The Kalman Filter and Extended Kalman Filter theory and practical applications are presented. Significant hands-on examples are used to reinforce the lectures.

Day 1
09.00 Introduction to Probability, Stochastic Processes and Signals, (Basic Theorems, Disturbances & Noise Representation)
09.45 Hands-on Session: Implementation of Disturbance & Noise in State-Space Model
11.00 Introduction to Kalman Filter (Continuous and Discrete Time)
12.00 Discrete Time Kalman Filter (Derivation, Properties, Riccati Equation and Tuning)
12.45 LUNCH
13.30 Hands-on Session: Application of Observers & Building the Kalman Filter
14.30 Introduction to Time Varying and Nonlinear Systems
15.15 Parameter Estimation using Extended Kalman Filters (Condition Monitoring, Model Based Fault Detection Methods)
16.00 Hands-on Session: Kalman Filtering for Parameter Estimation
17.00 CLOSE