Introduction to Estimation and Kalman Filtering

14th October 2016 2016, Glasgow

ISC is pleased to announce that registration is now open for this course. We are offering an "Early Bird Discount" if registration is received 2 weeks before the course start date (see online registration form). Employees of ACTC member companies are entitled to two places free of charge. Note that all courses offered can also be provided at your company premises through special arrangements, please contact us for more information.

Registration is required. Please ensure the payment details section of the form is completed.


This one-day course is aimed at introducing Kalman Filter algorithm and implementation as an observer and parameter estimation. This is particular useful when one consider the modelling of linear and/or nonlinear control systems. 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 lecture.

Additional Information
Event Venue

Premier Meetings Glasgow City Centre Buchanan Galleries, 141 West Nile Street, Glasgow, Glasgow, G1 2RN, United Kingdom


Glasgow City Centre offers a wide range of accommodation, you can find our recommendations here.

08.45 Registration
09.00 Introduction to Probability, Stochastic Processes and Signals
09.45 Hands-on Session: Implementation of Disturbance & Noise in State-Space Model
11.00 Introduction to Kalman Filter
12.00 Discrete Time Kalman Filter
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
16.00 Hands-on Session: Kalman Filtering for Parameter Estimation
17.00 CLOSE