Introduction to Estimation & Kalman Filter
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.
|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)|
|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|